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faisito/xlm-roberta-base-finetuned-panx-de-fr | faisito | xlm-roberta | 9 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,320 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1615
- F1: 0.8597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2876 | 1.0 | 715 | 0.1877 | 0.8274 |
| 0.1472 | 2.0 | 1430 | 0.1573 | 0.8508 |
| 0.0951 | 3.0 | 2145 | 0.1615 | 0.8597 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
| 727390b84efb0b08581595e5a90cd99a |
fanzru/bart-base-finetuned-xlsum-concat-multi-news | fanzru | bart | 21 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,462 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-xlsum-concat-multi-news
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8355
- Rouge1: 36.5801
- Rouge2: 15.2796
- Rougel: 29.8088
- Rougelsum: 29.8631
- Gen Len: 19.5457
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6985 | 1.0 | 20543 | 1.8355 | 36.5801 | 15.2796 | 29.8088 | 29.8631 | 19.5457 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
| b54df0dc5a4d3d6cb99eb611a9ec27af |
Ryosuke/an2-stable-diffusion | Ryosuke | null | 20 | 41 | diffusers | 2 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 914 | false | ### an2-stable-diffusion Dreambooth model trained by Ryosuke with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
00074-3537062306-portrait
| 135f2ce99c92d99e708a3d9b71f374de |
pardeep/distilbert-base-uncased-finetuned-emotion-ch02 | pardeep | distilbert | 14 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,342 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-ch02
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1703
- Accuracy: 0.934
- F1: 0.9342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2923 | 1.0 | 250 | 0.2001 | 0.9275 | 0.9263 |
| 0.1485 | 2.0 | 500 | 0.1703 | 0.934 | 0.9342 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 9f219efca827b8ea0616c4e7457b7a38 |
gokuls/bert-tiny-emotion-KD-distilBERT | gokuls | bert | 13 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,230 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-emotion-KD-distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5444
- Accuracy: 0.913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2533 | 1.0 | 1000 | 2.8358 | 0.7675 |
| 2.3274 | 2.0 | 2000 | 1.5893 | 0.8675 |
| 1.3974 | 3.0 | 3000 | 1.0286 | 0.891 |
| 0.9035 | 4.0 | 4000 | 0.7534 | 0.8955 |
| 0.6619 | 5.0 | 5000 | 0.6350 | 0.905 |
| 0.5482 | 6.0 | 6000 | 0.6180 | 0.899 |
| 0.4937 | 7.0 | 7000 | 0.5448 | 0.91 |
| 0.4013 | 8.0 | 8000 | 0.5493 | 0.906 |
| 0.3839 | 9.0 | 9000 | 0.5481 | 0.9095 |
| 0.3281 | 10.0 | 10000 | 0.5528 | 0.9115 |
| 0.3098 | 11.0 | 11000 | 0.5864 | 0.9095 |
| 0.2762 | 12.0 | 12000 | 0.5566 | 0.9095 |
| 0.2467 | 13.0 | 13000 | 0.5444 | 0.913 |
| 0.2286 | 14.0 | 14000 | 0.5306 | 0.912 |
| 0.2215 | 15.0 | 15000 | 0.5312 | 0.9115 |
| 0.2038 | 16.0 | 16000 | 0.5242 | 0.912 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
| 81974a4180d4fb573a7f57d104ec72c7 |
hugsao123/XLM-R-fine-tuned-for-ner | hugsao123 | xlm-roberta | 23 | 13 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,734 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-R-fine-tuned-for-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- F1: 0.8378
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4202 | 1.0 | 2500 | 0.3449 | 0.7963 |
| 0.2887 | 2.0 | 5000 | 0.2756 | 0.8057 |
| 0.2309 | 3.0 | 7500 | 0.2971 | 0.8040 |
| 0.1832 | 4.0 | 10000 | 0.3319 | 0.8167 |
| 0.1461 | 5.0 | 12500 | 0.3958 | 0.8350 |
| 0.114 | 6.0 | 15000 | 0.4087 | 0.8316 |
| 0.0833 | 7.0 | 17500 | 0.4320 | 0.8361 |
| 0.0614 | 8.0 | 20000 | 0.4885 | 0.8353 |
| 0.039 | 9.0 | 22500 | 0.5408 | 0.8390 |
| 0.0251 | 10.0 | 25000 | 0.5679 | 0.8378 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| f3f435e0cfa2515723077d6050a2feb7 |
mfleck/wav2vec2-large-xls-r-300m-slowenian-with-lm | mfleck | wav2vec2 | 11 | 6 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,025 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-slowenian-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3935
- Wer: 0.3480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.9937 | 2.5 | 100 | 3.1565 | 1.0 |
| 3.0466 | 5.0 | 200 | 3.0009 | 0.9992 |
| 2.9708 | 7.5 | 300 | 2.9494 | 0.9992 |
| 2.0519 | 10.0 | 400 | 0.8874 | 0.7290 |
| 0.5773 | 12.5 | 500 | 0.5258 | 0.5037 |
| 0.3427 | 15.0 | 600 | 0.4767 | 0.4649 |
| 0.2612 | 17.5 | 700 | 0.4549 | 0.4209 |
| 0.212 | 20.0 | 800 | 0.4294 | 0.3860 |
| 0.1748 | 22.5 | 900 | 0.4085 | 0.3769 |
| 0.1587 | 25.0 | 1000 | 0.4017 | 0.3673 |
| 0.1435 | 27.5 | 1100 | 0.3927 | 0.3538 |
| 0.1314 | 30.0 | 1200 | 0.3935 | 0.3480 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 18b192192bd4c54013f62c5128878cd2 |
versae/wav2vec2-base-coscan-no-area | versae | wav2vec2 | 10 | 3 | transformers | 0 | audio-classification | true | false | false | apache-2.0 | null | ['coscan-speech2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,267 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-coscan-no-area
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3398
- Accuracy: 0.9486
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0015 | 1.0 | 6468 | 0.3398 | 0.9486 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1 | e5ed37f5e7c095b4d0b3e7d10f58966d |
tomekkorbak/wonderful_keller | tomekkorbak | gpt2 | 23 | 1 | transformers | 0 | null | true | false | false | mit | ['en'] | ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 8,757 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wonderful_keller
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'filter_threshold': 0.00078,
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'wonderful_keller',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/354p6jl4 | 7bb4580e02a1c4579954133603c11318 |
jonatasgrosman/exp_w2v2t_sv-se_xlsr-53_s624 | jonatasgrosman | wav2vec2 | 10 | 8 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['sv-SE'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'sv-SE'] | false | true | true | 467 | false | # exp_w2v2t_sv-se_xlsr-53_s624
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 8185adc4cb363c39d3bba222f6a5cda1 |
VanHoan/bert-finetuned-squad | VanHoan | bert | 16 | 3 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 954 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| 40b9c5428d3e47a47db2b0b397b60c10 |
Freazling/test-sentiment-model-imdb-3000-samples | Freazling | distilbert | 10 | 5 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,058 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-sentiment-model-imdb-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3296
- Accuracy: 0.86
- F1: 0.8618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221113
- Datasets 2.6.1
- Tokenizers 0.13.2
| f3132be95197cb270f03856ca76676f1 |
clementchadebec/reproduced_ciwae | clementchadebec | null | 7 | 0 | pythae | 0 | null | false | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['pythae', 'reproducibility'] | false | true | true | 678 | false |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_ciwae")
```
## Reproducibility
This trained model reproduces the results of the official implementation of [1].
| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| CIWAE (beta=0.05) | Dyn. Binarized MNIST | NLL (5000 IS) | 84.74 (0.01) | 84.57 (0.09) |
[1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018. | 999cff5b080b26c35a77c07a9d71ebcd |
pyf98/slurp_entity_conformer | pyf98 | null | 21 | 2 | espnet | 0 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['en'] | ['slurp_entity'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | true | true | 11,767 | false |
## ESPnet2 ASR model
### `pyf98/slurp_entity_conformer`
This model was trained by Yifan Peng using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 55b6cc387fd0252d1a06db2042fd101bcea7bb34
pip install -e .
cd egs2/slurp_entity/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/slurp_entity_conformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu May 26 14:51:29 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202204`
- pytorch version: `pytorch 1.11.0`
- Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6`
- Commit date: `Thu May 26 00:22:45 2022 -0400`
## asr_train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k_raw_en_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/devel|8690|178058|82.9|7.8|9.3|2.7|19.8|51.5|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|262176|81.9|7.8|10.3|2.6|20.7|50.7|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/devel|8690|847400|89.4|3.1|7.5|3.1|13.7|51.5|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|1245475|88.4|3.1|8.5|3.0|14.6|50.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- kaldi_ark
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/devel/wav.scp
- speech
- kaldi_ark
- - dump/raw/devel/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 35000
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- E
- list
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- O
- U
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- descriptor
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- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: '202204'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| a94fd6cb60ec4a0d8e173a09cf4248e1 |
Alexwww/jason-art | Alexwww | null | 18 | 181 | diffusers | 1 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 505 | false | ### Jason-Art Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Putting the prompte words: "photography minimal symmetric" will help get better outputs
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| 6457cf3d849f4064e2e1fd1cba1ccc0e |
jacob-valdez/blenderbot-small-tflite | jacob-valdez | null | 3 | 0 | null | 0 | null | false | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['Android', 'tflite', 'blenderbot'] | false | true | true | 2,318 | false | # Model Card
`blenderbot-small-tflite` is a tflite version of `blenderbot-small-90M` I converted for my UTA CSE3310 class. See the repo at [https://github.com/kmosoti/DesparadosAEYE](https://github.com/kmosoti/DesparadosAEYE) and the conversion process [here](https://drive.google.com/file/d/1F93nMsDIm1TWhn70FcLtcaKQUynHq9wS/view?usp=sharing).
You have to right pad your user and model input integers to make them [32,]-shaped. Then indicate te true length with the 3rd and 4th params.
```python
display(interpreter.get_input_details())
display(interpreter.get_output_details())
```
```json
[{'dtype': numpy.int32,
'index': 0,
'name': 'input_tokens',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([32], dtype=int32),
'shape_signature': array([32], dtype=int32),
'sparsity_parameters': {}},
{'dtype': numpy.int32,
'index': 1,
'name': 'decoder_input_tokens',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([32], dtype=int32),
'shape_signature': array([32], dtype=int32),
'sparsity_parameters': {}},
{'dtype': numpy.int32,
'index': 2,
'name': 'input_len',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([], dtype=int32),
'shape_signature': array([], dtype=int32),
'sparsity_parameters': {}},
{'dtype': numpy.int32,
'index': 3,
'name': 'decoder_input_len',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([], dtype=int32),
'shape_signature': array([], dtype=int32),
'sparsity_parameters': {}}]
[{'dtype': numpy.int32,
'index': 3113,
'name': 'Identity',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([1], dtype=int32),
'shape_signature': array([1], dtype=int32),
'sparsity_parameters': {}}]
``` | e74c557f7ab1daa1b65624d876636a46 |
Geotrend/distilbert-base-en-th-cased | Geotrend | distilbert | 6 | 5 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | ['multilingual'] | ['wikipedia'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,224 | false |
# distilbert-base-en-th-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-th-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-th-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request. | c8fb073bffb3c4b3185c8a2524528b14 |
DOOGLAK/Article_50v9_NER_Model_3Epochs_UNAUGMENTED | DOOGLAK | bert | 13 | 5 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['article50v9_wikigold_split'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,550 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v9_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7640
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 0.9810 | 0.0918 | 0.0044 | 0.0084 | 0.7772 |
| No log | 2.0 | 12 | 0.7952 | 0.0 | 0.0 | 0.0 | 0.7782 |
| No log | 3.0 | 18 | 0.7640 | 0.0 | 0.0 | 0.0 | 0.7782 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
| 3c2708e4ac86986c457442539f21bb01 |
ryanlai/distilbert-base-uncased-finetuned-imdb | ryanlai | distilbert | 22 | 1 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,318 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7122 | 1.0 | 157 | 2.4938 |
| 2.5808 | 2.0 | 314 | 2.4249 |
| 2.5267 | 3.0 | 471 | 2.4353 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1
| ede77133f9165ccfad8fb81f353aa7b1 |
domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22 | domenicrosati | deberta-v2 | 12 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classification', 'generated_from_trainer'] | true | true | true | 2,209 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22
This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2165 | 1.0 | 669 | 0.0218 | 0.9963 | 0.9973 |
| 0.0717 | 2.0 | 1338 | 0.0213 | 0.9964 | 0.9974 |
| 0.03 | 3.0 | 2007 | 0.0121 | 0.9983 | 0.9988 |
| 0.0165 | 4.0 | 2676 | 0.0147 | 0.9976 | 0.9982 |
| 0.0072 | 5.0 | 3345 | 0.0000 | 1.0 | 1.0 |
| 0.0055 | 6.0 | 4014 | 0.0000 | 1.0 | 1.0 |
| 0.0077 | 7.0 | 4683 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 8.0 | 5352 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 9.0 | 6021 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 10.0 | 6690 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 11.0 | 7359 | 0.0000 | 1.0 | 1.0 |
| 0.0 | 12.0 | 8028 | 0.0000 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| beaeb643a1629561765eec03c8ec5069 |
sayakpaul/glpn-nyu-finetuned-diode-221228-072509 | sayakpaul | glpn | 7 | 1 | transformers | 0 | depth-estimation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | true | true | true | 7,836 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# glpn-nyu-finetuned-diode-221228-072509
This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4012
- Mae: 0.4030
- Rmse: 0.6173
- Abs Rel: 0.3487
- Log Mae: 0.1574
- Log Rmse: 0.2110
- Delta1: 0.4308
- Delta2: 0.6997
- Delta3: 0.8249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 48
- seed: 2022
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
| 1.1571 | 1.0 | 72 | 0.6604 | 0.6233 | 0.8403 | 0.5125 | 0.3119 | 0.3691 | 0.1726 | 0.3423 | 0.4877 |
| 0.4895 | 2.0 | 144 | 0.4506 | 0.4460 | 0.6404 | 0.4241 | 0.1812 | 0.2299 | 0.3325 | 0.6053 | 0.7943 |
| 0.4709 | 3.0 | 216 | 0.4414 | 0.4370 | 0.6305 | 0.4243 | 0.1764 | 0.2253 | 0.3537 | 0.6145 | 0.7988 |
| 0.4436 | 4.0 | 288 | 0.4335 | 0.4324 | 0.6285 | 0.4045 | 0.1746 | 0.2245 | 0.3444 | 0.6506 | 0.8096 |
| 0.4656 | 5.0 | 360 | 0.4552 | 0.4515 | 0.6328 | 0.4614 | 0.1838 | 0.2307 | 0.3374 | 0.5762 | 0.7722 |
| 0.4482 | 6.0 | 432 | 0.4234 | 0.4166 | 0.6233 | 0.3805 | 0.1654 | 0.2179 | 0.4035 | 0.6623 | 0.8130 |
| 0.4099 | 7.0 | 504 | 0.4176 | 0.4185 | 0.6238 | 0.3676 | 0.1662 | 0.2150 | 0.3937 | 0.6589 | 0.8153 |
| 0.3987 | 8.0 | 576 | 0.4515 | 0.4431 | 0.6300 | 0.4497 | 0.1792 | 0.2283 | 0.3561 | 0.5906 | 0.7781 |
| 0.396 | 9.0 | 648 | 0.4235 | 0.4267 | 0.6347 | 0.3591 | 0.1716 | 0.2224 | 0.3934 | 0.6310 | 0.7963 |
| 0.3608 | 10.0 | 720 | 0.4312 | 0.4181 | 0.6227 | 0.4022 | 0.1666 | 0.2217 | 0.4014 | 0.6586 | 0.8173 |
| 0.3568 | 11.0 | 792 | 0.4322 | 0.4198 | 0.6183 | 0.4047 | 0.1674 | 0.2186 | 0.3870 | 0.6420 | 0.8071 |
| 0.3923 | 12.0 | 864 | 0.4225 | 0.4196 | 0.6294 | 0.3630 | 0.1668 | 0.2181 | 0.3910 | 0.6537 | 0.8151 |
| 0.3971 | 13.0 | 936 | 0.4086 | 0.4105 | 0.6219 | 0.3541 | 0.1614 | 0.2144 | 0.4234 | 0.6820 | 0.8144 |
| 0.372 | 14.0 | 1008 | 0.4127 | 0.4099 | 0.6172 | 0.3668 | 0.1612 | 0.2119 | 0.4046 | 0.6727 | 0.8260 |
| 0.3884 | 15.0 | 1080 | 0.4060 | 0.4074 | 0.6176 | 0.3528 | 0.1598 | 0.2119 | 0.4109 | 0.6925 | 0.8225 |
| 0.3616 | 16.0 | 1152 | 0.4078 | 0.4092 | 0.6198 | 0.3532 | 0.1615 | 0.2139 | 0.4162 | 0.6791 | 0.8186 |
| 0.3504 | 17.0 | 1224 | 0.4202 | 0.4320 | 0.6408 | 0.3613 | 0.1740 | 0.2261 | 0.3769 | 0.6301 | 0.7915 |
| 0.3823 | 18.0 | 1296 | 0.4328 | 0.4218 | 0.6182 | 0.4198 | 0.1684 | 0.2207 | 0.3916 | 0.6371 | 0.8113 |
| 0.3437 | 19.0 | 1368 | 0.4133 | 0.4138 | 0.6205 | 0.3638 | 0.1636 | 0.2162 | 0.3967 | 0.6761 | 0.8188 |
| 0.3739 | 20.0 | 1440 | 0.4040 | 0.4070 | 0.6187 | 0.3486 | 0.1594 | 0.2124 | 0.4214 | 0.6813 | 0.8214 |
| 0.3397 | 21.0 | 1512 | 0.4180 | 0.4300 | 0.6360 | 0.3601 | 0.1732 | 0.2239 | 0.3708 | 0.6362 | 0.8006 |
| 0.332 | 22.0 | 1584 | 0.4025 | 0.4050 | 0.6182 | 0.3505 | 0.1582 | 0.2114 | 0.4274 | 0.6909 | 0.8275 |
| 0.3552 | 23.0 | 1656 | 0.4120 | 0.4179 | 0.6305 | 0.3569 | 0.1650 | 0.2188 | 0.4002 | 0.6753 | 0.8102 |
| 0.3804 | 24.0 | 1728 | 0.4093 | 0.4111 | 0.6223 | 0.3594 | 0.1620 | 0.2152 | 0.4068 | 0.6851 | 0.8166 |
| 0.3519 | 25.0 | 1800 | 0.4039 | 0.4122 | 0.6237 | 0.3511 | 0.1621 | 0.2137 | 0.4109 | 0.6895 | 0.8171 |
| 0.3276 | 26.0 | 1872 | 0.4044 | 0.4117 | 0.6183 | 0.3533 | 0.1623 | 0.2127 | 0.3979 | 0.6824 | 0.8251 |
| 0.3167 | 27.0 | 1944 | 0.4091 | 0.4099 | 0.6189 | 0.3600 | 0.1613 | 0.2135 | 0.4069 | 0.6898 | 0.8218 |
| 0.3547 | 28.0 | 2016 | 0.4051 | 0.4055 | 0.6192 | 0.3521 | 0.1586 | 0.2119 | 0.4216 | 0.6921 | 0.8256 |
| 0.3297 | 29.0 | 2088 | 0.4025 | 0.4091 | 0.6215 | 0.3500 | 0.1605 | 0.2126 | 0.4155 | 0.6960 | 0.8224 |
| 0.3305 | 30.0 | 2160 | 0.4040 | 0.4045 | 0.6171 | 0.3507 | 0.1584 | 0.2120 | 0.4281 | 0.6938 | 0.8255 |
| 0.34 | 31.0 | 2232 | 0.4036 | 0.4082 | 0.6194 | 0.3492 | 0.1606 | 0.2132 | 0.4196 | 0.6851 | 0.8207 |
| 0.3507 | 32.0 | 2304 | 0.4057 | 0.4120 | 0.6245 | 0.3482 | 0.1619 | 0.2148 | 0.4195 | 0.6777 | 0.8172 |
| 0.3617 | 33.0 | 2376 | 0.4036 | 0.4098 | 0.6241 | 0.3477 | 0.1606 | 0.2141 | 0.4219 | 0.6871 | 0.8186 |
| 0.3268 | 34.0 | 2448 | 0.4015 | 0.4060 | 0.6197 | 0.3440 | 0.1593 | 0.2122 | 0.4326 | 0.6868 | 0.8211 |
| 0.3188 | 35.0 | 2520 | 0.4018 | 0.4032 | 0.6154 | 0.3504 | 0.1575 | 0.2107 | 0.4306 | 0.6952 | 0.8250 |
| 0.3286 | 36.0 | 2592 | 0.4046 | 0.4103 | 0.6237 | 0.3507 | 0.1611 | 0.2139 | 0.4179 | 0.6883 | 0.8173 |
| 0.3279 | 37.0 | 2664 | 0.3995 | 0.3993 | 0.6118 | 0.3460 | 0.1558 | 0.2091 | 0.4401 | 0.6979 | 0.8272 |
| 0.3439 | 38.0 | 2736 | 0.4052 | 0.4063 | 0.6196 | 0.3555 | 0.1590 | 0.2117 | 0.4207 | 0.6972 | 0.8256 |
| 0.3188 | 39.0 | 2808 | 0.4028 | 0.4028 | 0.6176 | 0.3482 | 0.1574 | 0.2112 | 0.4351 | 0.6916 | 0.8253 |
| 0.3334 | 40.0 | 2880 | 0.4059 | 0.4093 | 0.6218 | 0.3534 | 0.1607 | 0.2137 | 0.4201 | 0.6885 | 0.8217 |
| 0.3393 | 41.0 | 2952 | 0.4043 | 0.4048 | 0.6193 | 0.3492 | 0.1584 | 0.2118 | 0.4300 | 0.6906 | 0.8246 |
| 0.3099 | 42.0 | 3024 | 0.4029 | 0.4041 | 0.6161 | 0.3499 | 0.1583 | 0.2118 | 0.4274 | 0.6966 | 0.8239 |
| 0.3339 | 43.0 | 3096 | 0.4032 | 0.4056 | 0.6213 | 0.3515 | 0.1584 | 0.2122 | 0.4257 | 0.6995 | 0.8239 |
| 0.3086 | 44.0 | 3168 | 0.4024 | 0.4049 | 0.6173 | 0.3509 | 0.1586 | 0.2120 | 0.4243 | 0.6994 | 0.8227 |
| 0.3262 | 45.0 | 3240 | 0.4007 | 0.4035 | 0.6185 | 0.3467 | 0.1575 | 0.2112 | 0.4304 | 0.6994 | 0.8246 |
| 0.3265 | 46.0 | 3312 | 0.4017 | 0.4033 | 0.6170 | 0.3495 | 0.1574 | 0.2110 | 0.4271 | 0.7043 | 0.8247 |
| 0.3324 | 47.0 | 3384 | 0.4015 | 0.4056 | 0.6192 | 0.3471 | 0.1587 | 0.2119 | 0.4281 | 0.6944 | 0.8220 |
| 0.3159 | 48.0 | 3456 | 0.4012 | 0.4036 | 0.6156 | 0.3487 | 0.1581 | 0.2114 | 0.4279 | 0.6982 | 0.8234 |
| 0.3238 | 49.0 | 3528 | 0.4017 | 0.4024 | 0.6161 | 0.3499 | 0.1571 | 0.2106 | 0.4304 | 0.7008 | 0.8255 |
| 0.3112 | 50.0 | 3600 | 0.4012 | 0.4030 | 0.6173 | 0.3487 | 0.1574 | 0.2110 | 0.4308 | 0.6997 | 0.8249 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 24a6164b6f6cd71802ce6e379bdf8d97 |
Dizzykong/Aristotle-8-29 | Dizzykong | gpt2 | 13 | 2 | transformers | 0 | text-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 903 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Aristotle-8-29
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| 025dd24244c6b6a2b8accbcf47fe9a2d |
zhihao/distilbert-base-uncased-finetuned-ner | zhihao | distilbert | 13 | 11 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | false | true | true | 1,554 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0615
- Precision: 0.9251
- Recall: 0.9363
- F1: 0.9307
- Accuracy: 0.9841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2473 | 1.0 | 878 | 0.0714 | 0.9154 | 0.9178 | 0.9166 | 0.9808 |
| 0.0522 | 2.0 | 1756 | 0.0620 | 0.9201 | 0.9348 | 0.9274 | 0.9832 |
| 0.031 | 3.0 | 2634 | 0.0615 | 0.9251 | 0.9363 | 0.9307 | 0.9841 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| ccc2e92fcb1f96a30999db45a0ea7f5b |
Huyen2310/Vin13000 | Huyen2310 | whisper | 17 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['vi'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | true | true | true | 1,024 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hieu Dam Model Shuffle
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Dataset by HieuDam dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 450
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 576b685678981bf6f959e8d19d7775ce |
it5/it5-base-repubblica-to-ilgiornale | it5 | t5 | 10 | 5 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['it'] | ['gsarti/change_it'] | {'emissions': '17g', 'source': 'Google Cloud Platform Carbon Footprint', 'training_type': 'fine-tuning', 'geographical_location': 'Eemshaven, Netherlands, Europe', 'hardware_used': '1 TPU v3-8 VM'} | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | true | true | true | 3,258 | false | # IT5 Base for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹
This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
r2g = pipeline("text2text-generation", model='it5/it5-base-repubblica-to-ilgiornale')
r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".")
>>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-repubblica-to-ilgiornale")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-repubblica-to-ilgiornale")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` | cadaf9c4c35ed8350cc698b4cb2befb6 |
jhonparra18/bert-base-uncased-cv-position-classifier | jhonparra18 | bert | 6 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,739 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-cv-position-classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6924
- Accuracy: {'accuracy': 0.5780703216130645}
- F1: {'f1': 0.5780703216130645}
- Precision: {'precision': 0.5780703216130645}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|
| 2.0336 | 1.14 | 1000 | 1.8856 | {'accuracy': 0.5259123479420097} | {'f1': 0.5259123479420097} | {'precision': 0.5259123479420097} |
| 1.5348 | 2.28 | 2000 | 1.6924 | {'accuracy': 0.5780703216130645} | {'f1': 0.5780703216130645} | {'precision': 0.5780703216130645} |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.8.1+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1
| 961c02373c565f0e53ff39e5f53041ee |
Helsinki-NLP/opus-mt-zne-es | Helsinki-NLP | marian | 10 | 7 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-zne-es
* source languages: zne
* target languages: es
* OPUS readme: [zne-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zne.es | 21.1 | 0.382 |
| 11c651097673eb64d885f00c1410b3d5 |
Helsinki-NLP/opus-mt-fr-ar | Helsinki-NLP | marian | 11 | 162 | transformers | 0 | translation | true | true | false | apache-2.0 | ['fr', 'ar'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 2,160 | false |
### fra-ara
* source group: French
* target group: Arabic
* OPUS readme: [fra-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ara/README.md)
* model: transformer
* source language(s): fra
* target language(s): apc ara arq arq_Latn ary arz
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.fra.ara | 14.4 | 0.439 |
### System Info:
- hf_name: fra-ara
- source_languages: fra
- target_languages: ara
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ara/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['fr', 'ar']
- src_constituents: {'fra'}
- tgt_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ara/opus-2020-07-03.test.txt
- src_alpha3: fra
- tgt_alpha3: ara
- short_pair: fr-ar
- chrF2_score: 0.439
- bleu: 14.4
- brevity_penalty: 1.0
- ref_len: 7956.0
- src_name: French
- tgt_name: Arabic
- train_date: 2020-07-03
- src_alpha2: fr
- tgt_alpha2: ar
- prefer_old: False
- long_pair: fra-ara
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | b7f30356b0eba76c43c0c92ff04138f4 |
Helsinki-NLP/opus-mt-de-ca | Helsinki-NLP | marian | 11 | 31 | transformers | 0 | translation | true | true | false | apache-2.0 | ['de', 'ca'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 1,996 | false |
### deu-cat
* source group: German
* target group: Catalan
* OPUS readme: [deu-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-cat/README.md)
* model: transformer-align
* source language(s): deu
* target language(s): cat
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.deu.cat | 37.4 | 0.582 |
### System Info:
- hf_name: deu-cat
- source_languages: deu
- target_languages: cat
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-cat/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['de', 'ca']
- src_constituents: {'deu'}
- tgt_constituents: {'cat'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-cat/opus-2020-06-16.test.txt
- src_alpha3: deu
- tgt_alpha3: cat
- short_pair: de-ca
- chrF2_score: 0.5820000000000001
- bleu: 37.4
- brevity_penalty: 0.956
- ref_len: 5507.0
- src_name: German
- tgt_name: Catalan
- train_date: 2020-06-16
- src_alpha2: de
- tgt_alpha2: ca
- prefer_old: False
- long_pair: deu-cat
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | 5d063c85d9cdacd20278383e75e8c8f2 |
SCUT-DLVCLab/lilt-roberta-en-base | SCUT-DLVCLab | lilt | 9 | 6,242 | transformers | 8 | feature-extraction | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision'] | false | true | true | 2,042 | false |
# LiLT-RoBERTa (base-sized model)
Language-Independent Layout Transformer - RoBERTa model by stitching a pre-trained RoBERTa (English) and a pre-trained Language-Independent Layout Transformer (LiLT) together. It was introduced in the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Wang et al. and first released in [this repository](https://github.com/jpwang/lilt).
Disclaimer: The team releasing LiLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Language-Independent Layout Transformer (LiLT) allows to combine any pre-trained RoBERTa encoder from the hub (hence, in any language) with a lightweight Layout Transformer to have a LayoutLM-like model for any language.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg" alt="drawing" width="600"/>
## Intended uses & limitations
The model is meant to be fine-tuned on tasks like document image classification, document parsing and document QA. See the [model hub](https://huggingface.co/models?search=lilt) to look for fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/lilt.html).
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.13669,
doi = {10.48550/ARXIV.2202.13669},
url = {https://arxiv.org/abs/2202.13669},
author = {Wang, Jiapeng and Jin, Lianwen and Ding, Kai},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` | 6a1a99a937a0cf8c625fcde2825794ac |
timm/convnext_pico.d1_in1k | timm | null | 4 | 94 | timm | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagenet-1k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'timm'] | false | true | true | 21,334 | false | # Model card for convnext_pico.d1_in1k
A ConvNeXt image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 9.0
- GMACs: 1.4
- Activations (M): 6.1
- Image size: 224 x 224
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/rwightman/pytorch-image-models
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(
urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model('convnext_pico.d1_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(
urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model(
'convnext_pico.d1_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for convnext_base:
# torch.Size([1, 128, 56, 56])
# torch.Size([1, 256, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 1024, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(
urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model(
'convnext_pico.d1_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, num_features, H, W) tensor
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
### By Top-1
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
|model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
|[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
|[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
|[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
|[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
|[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
|[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
|[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
|[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
|[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
|[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
|[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
|[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
|[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
|[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
|[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
|[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
|[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
|[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
|[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
|[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
|[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
|[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
|[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
|[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
|[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
|[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
|[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
|[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
|[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
|[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
|[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
|[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
|[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
|[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
|[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
|[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
|[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
|[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
|[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
|[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
|[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
|[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
|[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
|[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
|[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
|[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
|[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
|[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
|[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
### By Throughput (samples / sec)
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
|model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
|[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
|[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
|[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
|[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
|[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
|[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
|[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
|[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
|[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
|[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
|[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
|[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
|[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
|[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
|[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
|[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
|[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
|[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
|[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
|[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
|[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
|[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
|[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
|[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
|[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
|[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
|[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
|[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
|[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
|[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
|[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
|[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
|[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
|[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
|[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
|[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
|[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
|[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
|[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
|[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
|[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
|[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
|[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
|[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
|[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
|[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
|[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
|[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
|[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
| d7ac7bffbe58db78cfa1200ac0beaf15 |
nandysoham/1-clustered | nandysoham | distilbert | 8 | 34 | transformers | 0 | question-answering | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 2,073 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nandysoham/1-clustered
This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7785
- Train End Logits Accuracy: 0.7917
- Train Start Logits Accuracy: 0.7264
- Validation Loss: 0.9514
- Validation End Logits Accuracy: 0.7734
- Validation Start Logits Accuracy: 0.7014
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 138, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.1245 | 0.6957 | 0.6322 | 0.9694 | 0.7590 | 0.6906 | 0 |
| 0.7785 | 0.7917 | 0.7264 | 0.9514 | 0.7734 | 0.7014 | 1 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
| 81dea1a0ea1ed40baa977f7ad5c4e121 |
Sarahliu186/wav2vec2-base-timit-demo-colab | Sarahliu186 | wav2vec2 | 12 | 9 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,014 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| 2cf5d142b11dfd6b6c5246030f287cce |
muhtasham/small-mlm-glue-rte-target-glue-rte | muhtasham | bert | 10 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,438 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-glue-rte-target-glue-rte
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2116
- Accuracy: 0.6029
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4044 | 6.41 | 500 | 1.3568 | 0.6354 |
| 0.0595 | 12.82 | 1000 | 2.2538 | 0.6209 |
| 0.0294 | 19.23 | 1500 | 2.6675 | 0.6209 |
| 0.0158 | 25.64 | 2000 | 3.2116 | 0.6029 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 721c173b1c3886b94388360767836b29 |
merve/UCI_Credit_Cardyi6q1ptm-PAY_0-classification | merve | null | 4 | 0 | sklearn | 0 | tabular-classification | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tabular-classification', 'baseline-trainer'] | false | true | true | 11,020 | false |
## Baseline Model trained on UCI_Credit_Cardyi6q1ptm to apply classification on PAY_0
**Metrics of the best model:**
accuracy 0.715467
recall_macro 0.777916
precision_macro 0.578960
f1_macro 0.596625
Name: DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01), dtype: float64
**See model plot below:**
<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ......
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced',min_impurity_decrease=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ......
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced',min_impurity_decrease=0.01))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
LIMIT_BAL False False ... False False
SEX False False ... False False
EDUCATION False False ... False False
MARRIAGE False False ... False False
AGE False False ... False False
PAY_2 False False ... False False
PAY_3 False False ... False False
PAY_4 False False ... False False
PAY_5 False False ... False False
PAY_6 False False ... False Fal...
BILL_AMT3 True False ... False False
BILL_AMT4 True False ... False False
BILL_AMT5 True False ... False False
BILL_AMT6 True False ... False False
PAY_AMT1 True False ... False False
PAY_AMT2 True False ... False False
PAY_AMT3 True False ... False False
PAY_AMT4 True False ... False False
PAY_AMT5 True False ... False False
PAY_AMT6 True False ... False False
default.payment.next.month False False ... False False[23 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt | dce1dfa9f352728011c4e8371dc73fbc |
malloc/OpenNMT-py-German-English-2-layer-BiLSTM | malloc | null | 3 | 0 | null | 0 | translation | true | false | false | mit | ['de', 'en'] | ['IWSLT ‘14 DE-EN'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation', 'pytorch'] | false | true | true | 452 | false | # OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for German to English translation.
- Configuration: 2-layer BiLSTM with hidden size 500 trained for 20 epochs
- Data: IWSLT ‘14 DE-EN
- BLEU: 30.33 | 2aa6125a99c197823051205f2afb3e65 |
emrevarol/dz_finetuning-medium-distillbert-95K | emrevarol | distilbert | 13 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,100 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dz_finetuning-medium-distillbert-95K
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0047
- Accuracy: 0.9991
- F1: 0.9991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
| c49da6cb127bddb55883c9f6dd527bc4 |
l3cube-pune/hi-least-ht-1m | l3cube-pune | bert | 8 | 4 | transformers | 0 | fill-mask | true | false | false | cc-by-4.0 | ['hi'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 548 | false |
A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on least hateful Hindi Tweets.<br>
More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br>
```
@article{gokhale2022spread,
title={Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection},
author={Gokhale, Omkar and Kane, Aditya and Patankar, Shantanu and Chavan, Tanmay and Joshi, Raviraj},
journal={arXiv preprint arXiv:2210.04267},
year={2022}
}
``` | dab6ee5486f41127ee34a2f986ca81b6 |
anmol-chawla/dyc0002 | anmol-chawla | null | 15 | 4 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 614 | false | ### dyc0002 Dreambooth model trained by anmol-chawla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
| 297a6860cf1c4cfa64d26164a482f303 |
sshleifer/distilbart-cnn-6-6 | sshleifer | bart | 9 | 11,117 | transformers | 16 | summarization | true | false | true | apache-2.0 | ['en'] | ['cnn_dailymail', 'xsum'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | false | true | true | 1,536 | false |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L |
|:---------------------------|------------:|----------------------:|----------:|----------:|----------:|
| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 |
| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 |
| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 |
| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 |
| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 |
| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 |
| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 |
| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 |
| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 |
| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |
| 3b59ab886d3e0ad6dfa27f1ca72822ce |
bheshaj/bart-large-cnn-small-billsum-5epochs | bheshaj | bart | 10 | 133 | transformers | 0 | text2text-generation | true | false | false | mit | null | ['billsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,678 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-small-billsum-5epochs
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7206
- Rouge1: 0.5406
- Rouge2: 0.312
- Rougel: 0.3945
- Rougelsum: 0.4566
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3.373e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.3723 | 1.33 | 16 | 1.8534 | 0.5204 | 0.299 | 0.3893 | 0.4441 |
| 1.6579 | 2.67 | 32 | 1.7208 | 0.5427 | 0.3143 | 0.3915 | 0.459 |
| 1.2397 | 4.0 | 48 | 1.7206 | 0.5406 | 0.312 | 0.3945 | 0.4566 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
| 49c385bc3654d64ba54b6bedbbeddcf2 |
HURIDOCS/pdf-segmetation | HURIDOCS | null | 8 | 0 | null | 0 | null | false | false | false | openrail | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 726 | false |
<h3 align="center">PDF Paragraphs Extraction</h3>
<p align="center">A model for extracting paragraphs from PDFs</p>
This model uses features from the PDF to extract the text and paragraphs from it. It can be used as a service.
The paragraphs contain the page number, the position in the page, the size, and the text.
## Quick Start
Download the service that uses the model:
git clone https://github.com/huridocs/pdf_paragraphs_extraction.git
cd pdf_paragraphs_extraction
Start the service:
./run start
Get the paragraphs from a PDF:
curl -X GET -F 'file=@/PATH/TO/PDF/pdf_name.pdf' localhost:5051
To stop the server:
./run stop
## Performance
Accuracy: 93.9%
Speed: 0.15 seconds per page | 1a5f32f2a44acdc017ec1620f3b04c68 |
jonatasgrosman/exp_w2v2t_pl_hubert_s6 | jonatasgrosman | hubert | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['pl'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'pl'] | false | true | true | 450 | false | # exp_w2v2t_pl_hubert_s6
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| ef8606c705af0810ca842d0002e55208 |
Helsinki-NLP/opus-mt-es-sg | Helsinki-NLP | marian | 10 | 15 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 768 | false |
### opus-mt-es-sg
* source languages: es
* target languages: sg
* OPUS readme: [es-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-sg/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-sg/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.es.sg | 24.8 | 0.435 |
| 6e8278b7ec9eb955d85225ac0e3e3924 |
google/t5-efficient-xl-nl6 | google | t5 | 12 | 7 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['en'] | ['c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['deep-narrow'] | false | true | true | 6,242 | false |
# T5-Efficient-XL-NL6 (Deep-Narrow version)
T5-Efficient-XL-NL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-xl-nl6** - is of model type **Xl** with the following variations:
- **nl** is **6**
It has **737.59** million parameters and thus requires *ca.* **2950.37 MB** of memory in full precision (*fp32*)
or **1475.18 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future. | ad8cda3dde6aecce758d009b37a0137b |
thangcv/distilbert-base-uncased-finetuned-emotion | thangcv | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,343 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2156
- Accuracy: 0.924
- F1: 0.9243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8151 | 1.0 | 250 | 0.3062 | 0.9115 | 0.9089 |
| 0.2428 | 2.0 | 500 | 0.2156 | 0.924 | 0.9243 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 9b85d62088c7affa7cf85e10aa90fba6 |
bigmorning/bigmorning_whisper | bigmorning | whisper | 11 | 6 | transformers | 0 | automatic-speech-recognition | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 999 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bigmorning_whisper
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
| a9a82ef3181cd3c668593918ef489b73 |
bugdaryan/distilbert-base-uncased-finetuned-squad | bugdaryan | distilbert | 14 | 3 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad_v2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 931 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 227e3d5fefb808faa3f333ee46cba6de |
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3 | theojolliffe | bart | 13 | 1 | transformers | 0 | text2text-generation | true | false | false | mit | null | ['scientific_papers'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,592 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1265
- Rouge1: 41.9656
- Rouge2: 15.3793
- Rougel: 24.0382
- Rougelsum: 37.6057
- Gen Len: 130.8531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.1485 | 1.0 | 33840 | 2.1265 | 41.9656 | 15.3793 | 24.0382 | 37.6057 | 130.8531 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| bb38c1ff76c7fa24fd4932eef4875bc0 |
lingwave-admin/state-op-detector | lingwave-admin | distilbert | 9 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['classification'] | false | true | true | 2,934 | false |
# State Social Operator Detector
## Overview
State-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence [elections](https://www.brennancenter.org/our-work/analysis-opinion/new-evidence-shows-how-russias-election-interference-has-gotten-more) are most prominent in the news, but other campaigns have been identified, with the intent to [turn South American countries against the US](https://www.nbcnews.com/news/latino/russia-disinformation-ukraine-spreading-spanish-speaking-media-rcna22843), spread disinformation on the [invasion of Ukraine](https://www.forbes.com/sites/petersuciu/2022/03/10/russian-sock-puppets-spreading-misinformation-on-social-media-about-ukraine/), and foment conflict in America's own culture wars by [influencing all sides](https://journals.sagepub.com/doi/10.1177/19401612221082052) as part of an effort to weaken America's hegemonic status.
Iranian and [Chinese](https://www.bbc.com/news/56364952) efforts are also well-funded, though not as widespread or aggressive as those of Russia. Even so, Chinese influence is growing, and often it uses social media to spread specific narratives on [Xinjiang and the Uyghur situation](https://www.lawfareblog.com/understanding-pro-china-propaganda-and-disinformation-tool-set-xinjiang), Hong Kong, COVID-19, and Taiwan as well as sometimes supporting [Russian efforts](https://www.brookings.edu/techstream/china-and-russia-are-joining-forces-to-spread-disinformation/).
We need better tools to combat this disinformation, both for social media administrators as well as the public. As part of an effort towards that, we have created a proof-of-concept tool that can be operated via browser extension to identify likely state-funded social media operators on Twitter through inference performed on tweet content.
The core of the tool is a DistilBERT language transformer model that has been finetuned on 250K samples of known state operator tweets and natural tweets pulled from the Twitter API. It is highly accurate at distinguishing normal users from state operators (99%), but has some limitations due to sampling recency bias. We intend to iteratively improve the model as time goes on.
## Usage
You can try out the model by entering in a sequence of 1-10 tweets. Each should be separated by pipes, as follows: "this is tweet one | this is tweet two." The model will then classify the sequence as belonging to a state operator or a normal user.
## Further Information
You can obtain further information on the data collection and training used to create this model at the following Github repo: [State Social Operator Detection](https://github.com/curt-tigges/state-social-operator-detection)
## Contact
You can reach me at projects@curttigges.com. | 7897b4fff18930613f3ef248c7d264e4 |
pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP | pszemraj | longt5 | 15 | 30 | transformers | 0 | summarization | true | false | false | apache-2.0 | null | ['kmfoda/booksum'] | null | 8 | 0 | 6 | 2 | 1 | 1 | 0 | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | true | true | true | 1,309 | false |
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
> NOTE: this is still a work-in-progress (WIP) and not completed/converged by any means, but sharing to maybe save some time for others :)
## Updates
_As I update this WIP checkpoint, I will post a note here._
- July 26, 2022: add two more epochs of training, metrics starting to be _almost_ as good as the more-tuned `base` variant
- July 8, 2022: add checkpoint with ~4 epochs of training on A100, equating to approx 350 steps of functional batch size 128
- July 4, 2022: add checkpoint with six additional epochs of training with the dataset summary outputs filtered to 1024 **tokens**, resolving the prior issue of short summaries.
## About
- a checkpoint of [Stancld/longt5-tglobal-large-16384-pubmed-3k_steps](https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps) trained on `kmfoda/booksum` for about 26 epochs
- max input lengths during training vary between 8192 and 16384 tokens depending on GPU availability. This checkpoint was **trained with 16384 tokens as the max input length for the final 10+ epochs**
## Comparisons
- compare to [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary).
- **inference API has been disabled because it's too compute-intensive :/**
| 561318decea1c0441ae42c2de6fce6f9 |
kejian/literal-conditional | kejian | null | 2 | 0 | null | 0 | null | false | false | false | apache-2.0 | ['en'] | ['kejian/codeparrot-train-more-filter-3.3b-cleaned'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 5,566 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# literal-conditional
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.1,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'batch_size': 128,
'every_n_steps': 384,
'force_call_on': [12588],
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'bad_words_ids': [[32769]],
'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_hits_threshold': 0,
'num_samples': 2048,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'bad_words_ids': [[32769]],
'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_hits_threshold': 0,
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'every_n_steps': 384,
'force_call_on': [12588],
'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>',
'should_insert_prefix': False},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': 'cf05a2b0558c03b08c78f07662c22989785b9520'},
'num_additional_tokens': 2,
'path_or_name': 'kejian/mighty-mle'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'kejian/mighty-mle',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'literal-conditional',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 12588,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/4l2bk9f9 | ff1bd84b2aa16b5622256c6c6f336d87 |
kejian/vigor-awr | kejian | gpt2 | 36 | 0 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['kejian/codeparrot-train-more-filter-3.3b-cleaned'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 4,544 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vigor-awr
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 128,
'every_n_steps': 256,
'force_call_on': [6294],
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_hits_threshold': 0,
'num_samples': 2048},
{'display_as_html': True,
'generate_kwargs': {'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_hits_threshold': 0,
'num_samples': 2048,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'every_n_steps': 256,
'force_call_on': [6294],
'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'alpha': 0.05, 'beta': 1, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 256,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'vigor-awr',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 6294,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/1sojz3iz | 4c3201095882ae7641ffd5da3d9f8580 |
Panos/finetuning-sentiment-model-imdb | Panos | distilbert | 13 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,047 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2368
- Accuracy: 0.9309
- F1: 0.9316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| 09631a096dfc8b934d52105a72793f99 |
S1d-dha-nth3/ncert_bio | S1d-dha-nth3 | bert | 10 | 12 | transformers | 0 | fill-mask | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 2,088 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# S1d-dha-nth3/ncert_bio
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.6150
- Validation Loss: 2.5873
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -647, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5434 | 2.8928 | 0 |
| 2.9142 | 2.6476 | 1 |
| 2.6884 | 2.5008 | 2 |
| 2.6079 | 2.5775 | 3 |
| 2.5748 | 2.5737 | 4 |
| 2.6031 | 2.5074 | 5 |
| 2.6237 | 2.5028 | 6 |
| 2.5849 | 2.5862 | 7 |
| 2.6154 | 2.4751 | 8 |
| 2.5584 | 2.4866 | 9 |
| 2.6107 | 2.5268 | 10 |
| 2.5852 | 2.5659 | 11 |
| 2.5915 | 2.5768 | 12 |
| 2.5678 | 2.7020 | 13 |
| 2.6150 | 2.5873 | 14 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
| 7f42144d4d1bfa1be561965eebe80930 |
Zirk/wav2vec2-base-timit-demo-colab | Zirk | wav2vec2 | 16 | 9 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,067 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| 3076b8304fdc445872b951fac055fe56 |
SiraH/bert-finetuned-squad | SiraH | bert | 12 | 3 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 3 | 1 | 2 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 954 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| d27f2c4840be689e5f1b470226996257 |
TransQuest/siamesetransquest-da-multilingual | TransQuest | xlm-roberta | 11 | 5 | transformers | 0 | feature-extraction | true | false | false | apache-2.0 | ['multilingual-multilingual'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['Quality Estimation', 'siamesetransquest', 'da'] | false | true | true | 5,245 | false |
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-multilingual")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
| 9da5192e6ee8511cd8e580e76e6ace46 |
jonatasgrosman/exp_w2v2t_ja_hubert_s334 | jonatasgrosman | hubert | 10 | 8 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['ja'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'ja'] | false | true | true | 452 | false | # exp_w2v2t_ja_hubert_s334
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 3ebbec27b414e4ea052ae78f57ff7128 |
jayanta/cvt-13-384-in22k-FV-finetuned-memes | jayanta | cvt | 12 | 7 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagefolder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,230 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cvt-13-384-in22k-FV-finetuned-memes
This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5595
- Accuracy: 0.8346
- Precision: 0.8327
- Recall: 0.8346
- F1: 0.8322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.4066 | 0.99 | 20 | 1.2430 | 0.5124 | 0.5141 | 0.5124 | 0.4371 |
| 1.0813 | 1.99 | 40 | 0.8244 | 0.6893 | 0.6834 | 0.6893 | 0.6616 |
| 0.8392 | 2.99 | 60 | 0.6334 | 0.7612 | 0.7670 | 0.7612 | 0.7570 |
| 0.7065 | 3.99 | 80 | 0.5819 | 0.7767 | 0.7799 | 0.7767 | 0.7672 |
| 0.5751 | 4.99 | 100 | 0.5365 | 0.8176 | 0.8216 | 0.8176 | 0.8130 |
| 0.4896 | 5.99 | 120 | 0.4943 | 0.8308 | 0.8257 | 0.8308 | 0.8265 |
| 0.4487 | 6.99 | 140 | 0.5399 | 0.8107 | 0.8069 | 0.8107 | 0.8054 |
| 0.4349 | 7.99 | 160 | 0.4892 | 0.8300 | 0.8285 | 0.8300 | 0.8273 |
| 0.43 | 8.99 | 180 | 0.4984 | 0.8454 | 0.8465 | 0.8454 | 0.8426 |
| 0.4372 | 9.99 | 200 | 0.5573 | 0.8192 | 0.8221 | 0.8192 | 0.8157 |
| 0.3994 | 10.99 | 220 | 0.5158 | 0.8300 | 0.8284 | 0.8300 | 0.8281 |
| 0.3883 | 11.99 | 240 | 0.5495 | 0.8354 | 0.8317 | 0.8354 | 0.8314 |
| 0.406 | 12.99 | 260 | 0.5298 | 0.8284 | 0.8285 | 0.8284 | 0.8246 |
| 0.3355 | 13.99 | 280 | 0.5401 | 0.8393 | 0.8346 | 0.8393 | 0.8357 |
| 0.395 | 14.99 | 300 | 0.5915 | 0.8308 | 0.8278 | 0.8308 | 0.8261 |
| 0.3612 | 15.99 | 320 | 0.5852 | 0.8408 | 0.8378 | 0.8408 | 0.8368 |
| 0.3765 | 16.99 | 340 | 0.5509 | 0.8385 | 0.8351 | 0.8385 | 0.8356 |
| 0.3688 | 17.99 | 360 | 0.5668 | 0.8416 | 0.8398 | 0.8416 | 0.8387 |
| 0.3503 | 18.99 | 380 | 0.5626 | 0.8393 | 0.8371 | 0.8393 | 0.8365 |
| 0.3611 | 19.99 | 400 | 0.5595 | 0.8346 | 0.8327 | 0.8346 | 0.8322 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
| b67a1037ca8493a8af39e1b95d46347d |
DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 | DrishtiSharma | wav2vec2 | 19 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['sl'] | ['mozilla-foundation/common_voice_8_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'sl'] | true | true | true | 2,552 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2855
- Wer: 0.2401
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9294 | 6.1 | 500 | 2.9712 | 1.0 |
| 2.8305 | 12.2 | 1000 | 1.7073 | 0.9479 |
| 1.4795 | 18.29 | 1500 | 0.5756 | 0.6397 |
| 1.3433 | 24.39 | 2000 | 0.4968 | 0.5424 |
| 1.1766 | 30.49 | 2500 | 0.4185 | 0.4743 |
| 1.0017 | 36.59 | 3000 | 0.3303 | 0.3578 |
| 0.9358 | 42.68 | 3500 | 0.3003 | 0.3051 |
| 0.8358 | 48.78 | 4000 | 0.3045 | 0.2884 |
| 0.7647 | 54.88 | 4500 | 0.2866 | 0.2677 |
| 0.7482 | 60.98 | 5000 | 0.2829 | 0.2585 |
| 0.6943 | 67.07 | 5500 | 0.2782 | 0.2478 |
| 0.6586 | 73.17 | 6000 | 0.2911 | 0.2537 |
| 0.6425 | 79.27 | 6500 | 0.2817 | 0.2462 |
| 0.6067 | 85.37 | 7000 | 0.2910 | 0.2436 |
| 0.5974 | 91.46 | 7500 | 0.2875 | 0.2430 |
| 0.5812 | 97.56 | 8000 | 0.2852 | 0.2396 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| 21c952e9f6823f5af4095bf7d30e04c8 |
ScyKindness/Hatsune_Miku | ScyKindness | gpt2 | 9 | 4 | transformers | 0 | conversational | true | true | true | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['conversational'] | false | true | true | 2,309 | false |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
* Multi-turn generation examples from an interactive environment:
|Role | Response |
|---------|--------|
|User | Does money buy happiness? |
| Bot | Depends how much money you spend on it .|
|User | What is the best way to buy happiness ? |
| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
|User |This is so difficult ! |
| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
# pretty print last ouput tokens from bot
print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
| 7094ab3bad3e7ef044f127da58a3b265 |
Hate-speech-CNERG/hindi-codemixed-abusive-MuRIL | Hate-speech-CNERG | bert | 7 | 1 | transformers | 0 | text-classification | true | false | false | afl-3.0 | ['hi-en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 941 | false |
This model is used detecting **abusive speech** in **Code-Mixed Hindi**. It is finetuned on MuRIL model using code-mixed hindi abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~ | f850008bf982fa9387678b0b57b74cf3 |
sd-concepts-library/leica | sd-concepts-library | null | 9 | 0 | null | 3 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 978 | false | ### leica on Stable Diffusion
This is the `<leica>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
| 6195d463a2416cbc1528e016cfd39d48 |
muhtasham/tiny-mlm-glue-wnli-target-glue-stsb | muhtasham | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,962 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-wnli-target-glue-stsb
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8784
- Pearson: 0.7929
- Spearmanr: 0.7891
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 3.3443 | 2.78 | 500 | 1.5642 | 0.5784 | 0.6011 |
| 1.2259 | 5.56 | 1000 | 1.0907 | 0.7358 | 0.7382 |
| 0.8948 | 8.33 | 1500 | 0.9367 | 0.7750 | 0.7751 |
| 0.7357 | 11.11 | 2000 | 0.8525 | 0.7934 | 0.7905 |
| 0.6119 | 13.89 | 2500 | 0.8436 | 0.7977 | 0.7944 |
| 0.5301 | 16.67 | 3000 | 0.8999 | 0.7947 | 0.7928 |
| 0.4657 | 19.44 | 3500 | 0.8341 | 0.7989 | 0.7943 |
| 0.4104 | 22.22 | 4000 | 0.8818 | 0.7972 | 0.7930 |
| 0.3686 | 25.0 | 4500 | 0.8811 | 0.7973 | 0.7929 |
| 0.3348 | 27.78 | 5000 | 0.8784 | 0.7929 | 0.7891 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 065d485b97305981358adab6d7c09fab |
espnet/bengali_blstm | espnet | null | 22 | 0 | espnet | 0 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['noinfo'] | ['bn_openslr53'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | true | true | 13,360 | false |
## ESPnet2 ASR model
### `espnet/bengali_blstm`
This model was trained by dzeinali using bn_openslr53 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/bn_openslr53/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/bengali_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun May 22 21:21:37 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b`
- Commit date: `Thu Apr 28 19:50:59 2022 -0400`
## asr_bn_rnn
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_batch_size1_asr_model_valid.acc.best/sbn_test|2018|6470|79.8|17.9|2.3|2.2|22.4|43.3|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_batch_size1_asr_model_valid.acc.best/sbn_test|2018|39196|93.9|2.8|3.3|1.2|7.3|43.3|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_batch_size1_asr_model_valid.acc.best/sbn_test|2018|15595|84.8|8.9|6.3|1.0|16.2|43.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_bn_rnn
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe1000/train/speech_shape
- exp/asr_stats_raw_bpe1000/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe1000/valid/speech_shape
- exp/asr_stats_raw_bpe1000/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/sbn_train/wav.scp
- speech
- sound
- - dump/raw/sbn_train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/sbn_dev/wav.scp
- speech
- sound
- - dump/raw/sbn_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- র
- ে
- ন
- ের
- া
- ল
- ক
- ্
- ো
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- ▁ব
- ও
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- ▁ও
- ▁হ
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- তা
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- িত
- ▁থেকে
- ্যা
- ণ
- '-'
- ▁প্র
- তি
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- ▁পা
- ▁ক
- ঁ
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- ▁সা
- লা
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- ','
- ্র
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- ▁সে
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- ▁কা
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- ▁চ
- লি
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- ▁যে
- থ
- ।
- ড
- ুল
- িয়ে
- ▁গ
- বি
- ▁তা
- রি
- কা
- ▁র
- ▁ফ
- পা
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- য়ে
- ▁নিয়ে
- ▁তিনি
- ▁একটি
- নের
- ▁হয়েছে
- ্ব
- ▁ত
- ▁জন্য
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- বার
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- ান
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- জা
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- ঠ
- ুর
- ▁হবে
- ▁মি
- দা
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- ▁জা
- ▁বলে
- ▁কি
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- ▁ঘ
- ▁দু
- হা
- ত্র
- ০
- ছেন
- ▁কথা
- সি
- াম
- ▁ছিল
- ▁উ
- ▁বল
- ▁তাদের
- ৃ
- ▁রা
- ▁সঙ্গে
- ▁প্রতি
- ▁এবং
- ▁ধ
- ▁ল
- ছ
- ▁খা
- ▁বে
- ▁সময়
- য়া
- জন
- মি
- ন্ত
- ▁করতে
- ▁সু
- ▁করেন
- ীর
- ৌ
- ▁অনেক
- গুলো
- ষ্ট
- ধা
- সা
- ▁হয়ে
- ▁মধ্যে
- ▁চা
- ▁লা
- ির
- ▁১
- ▁সং
- োর
- ভাবে
- ▁আমি
- ১
- শা
- াল
- জি
- ▁তারা
- ▁যায়
- মান
- ▁কাজ
- ▁কিছু
- ▁দিয়ে
- টে
- রণ
- ▁ড
- ▁উপ
- স্থ
- দি
- সে
- ▁মে
- ▁সরকার
- ▁খ
- ▁পার
- ীয়
- ক্ত
- ওয়া
- স্ট
- এ
- ▁বাংলাদেশ
- ড়ে
- ন্ট
- ▁২
- ▁আছে
- ▁সব
- ছি
- ▁দি
- ▁আমার
- ▁এখন
- মে
- ▁বছর
- ▁ট
- ▁শা
- কি
- ন্ড
- ▁নাম
- ▁কোন
- দিন
- পুর
- ▁সম্
- ছিল
- ▁পুলিশ
- ▁য
- ৈ
- ▁মানুষ
- ▁দা
- েই
- ▁এর
- ▁সালে
- ▁কর
- ঘ
- গ্র
- ▁দিন
- ▁পারে
- ্ম
- ৫
- ▁দেশ
- ▁দেখ
- ▁স্ব
- ▁সম
- ▁১৯
- ▁সি
- ▁শুরু
- ▁প্রথম
- ত্
- ▁তো
- ্ট
- ▁আগে
- ▁কোনো
- ▁রয়েছে
- ▁হচ্ছে
- ▁অব
- ছিলেন
- যোগ
- জে
- ▁ভারত
- ▁নে
- প্র
- ▁সেই
- গা
- ▁গা
- হি
- ন্ন
- ▁ছ
- ▁জন
- ▁নির্
- খা
- পি
- ▁পে
- ▁স্
- াব
- ▁মো
- ▁অনু
- ▁কিন্তু
- ৯
- ▁পরি
- ▁ঢাকা
- তার
- লো
- ▁বিষয়
- ▁তাঁর
- ৪
- র্থ
- ▁অ্যা
- ▁ঘটনা
- ▁শেষ
- ড়ি
- লেন
- ▁আমাদের
- ▁বড়
- দেশ
- ▁নেই
- ▁ব্যা
- ানো
- ▁বেশি
- মার
- বাস
- ▁তবে
- ▁কো
- শি
- ▁বিভিন্ন
- ▁নয়
- ৭
- নী
- ৩
- ▁দল
- ▁দেখা
- ঝ
- ▁করার
- ▁কে
- ▁হলে
- ুক
- ▁গু
- ▁৩
- ৬
- ▁মনে
- ▁নির্বাচন
- ▁রাজ
- ▁করেছে
- ীন
- লের
- িতে
- ▁একটা
- ঞ্চ
- ▁রাখ
- ▁থাক
- ▁আমরা
- ▁চল
- ২
- ▁কাছে
- ▁মু
- ▁পড়
- ▁সহ
- ▁হিসেবে
- জ্ঞ
- ান্ত
- ণ্ড
- ৎ
- য়ের
- ▁পু
- ▁একজন
- ▁বলেন
- ুন
- িং
- ’
- ▁বাংলা
- টার
- ুম
- ঞ্জ
- ▁বাড়ি
- ▁গত
- ▁হাজার
- ▁মতো
- ডি
- ▁তিন
- দ্ধ
- ▁এমন
- ▁কয়েক
- ▁কম
- ত্ব
- ্রা
- ▁দিকে
- ▁ছিলেন
- ▁পড়ে
- নার
- ▁করি
- কাল
- ▁মুখ
- ▁উঠ
- র্ত
- ▁টাকা
- চার
- শে
- ▁এসে
- ▁দুই
- ▁করেছেন
- ▁লোক
- ম্প
- ৮
- ষ্ঠ
- ▁মহা
- ▁কু
- ▁থাকে
- বাদ
- চি
- ▁এলাকা
- ▁জানান
- ▁প্রায়
- ▁দেয়া
- ▁গেল
- য
- চ্ছে
- ▁ছবি
- ▁নতুন
- ▁অবস্থা
- ▁অভি
- ▁আজ
- ▁কার
- ▁খু
- ▁জানা
- ▁করছে
- টির
- ▁বাংলাদেশের
- ▁বন্ধ
- কারী
- ▁অন্য
- ▁ধরে
- প্ত
- ▁তাকে
- ▁গেছে
- ▁শি
- চা
- আ
- ▁চাল
- ▁আল
- ▁৫
- ▁উত্ত
- ▁ঝ
- ▁জীবন
- লার
- ঙ
- ▁প্রকাশ
- ▁মেয়ে
- ▁রে
- ▁দেশের
- ▁খেল
- ▁মূল
- ভি
- ঙ্ক
- ▁চি
- ▁পর্যন্ত
- ▁সাথে
- লাম
- ▁৪
- ▁টি
- ▁বো
- ▁আইন
- গত
- ▁হতে
- ▁ভালো
- .
- স্ক
- ▁অভিযোগ
- ন্স
- ▁কারণে
- ▁অর্থ
- ▁অপ
- ক্স
- বু
- ▁২০
- ▁পাওয়া
- ▁খুব
- ▁মন
- সম
- ল্লা
- ব্দ
- ▁পি
- ▁ওই
- ▁করবে
- য়ার
- সহ
- ক্ষণ
- ▁নারী
- ম্ব
- ▁ফা
- ▁বেশ
- ▁পেয়ে
- দে
- ▁তখন
- িয়ার
- ▁ক্যা
- ▁ছেলে
- ▁চার
- ভার
- ▁দিতে
- ▁ক্র
- ▁গান
- বাহিনী
- ▁ভি
- কৃত
- ▁গো
- বল
- ▁ইসলাম
- ▁জি
- ▁ডি
- ন্দ্র
- ▁গ্রাম
- ▁ওপর
- ▁ভোট
- ▁পাঠ
- ▁গিয়ে
- ▁মামলা
- ▁ব্যবস্থা
- সার
- যুক্ত
- ▁মাস
- দার
- ▁সেখানে
- ▁জন্ম
- ▁পদ
- ▁কেউ
- র্ণ
- ▁দেওয়া
- ভাগ
- ▁১০
- ▁উদ্
- োয়া
- রূপ
- ▁ফেল
- ▁তৈরি
- ▁খবর
- ▁কেন
- ▁ভাষা
- ▁৬
- ▁ভাব
- ▁নেতা
- ▁জানিয়েছে
- ▁কী
- ফা
- ▁থাকা
- ▁লি
- টের
- ▁ছা
- ▁হল
- ▁গ্র
- ▁কর্ম
- ▁সদস্য
- ▁জাতীয়
- ▁ব্র
- দু
- ▁কেন্দ্র
- ▁হওয়ার
- ▁দেব
- ▁চলে
- ▁হলো
- তু
- ▁বিশ্ব
- ▁যাওয়া
- ▁যাবে
- ▁ট্র
- ▁সম্পর্ক
- ▁দিয়েছে
- ▁যদি
- ▁বিরুদ্ধে
- ▁বিশেষ
- ▁করলে
- ▁ছোট
- ▁অধি
- ▁শুন
- ▁আবার
- ▁কারণ
- ▁দলের
- ▁ফি
- ▁স্ট
- ▁দেয়
- ▁শিল্প
- ▁রাজনৈতিক
- ▁বলা
- ▁ছাড়া
- ▁জেলা
- ▁দেখে
- ▁প্রধান
- ▁এসব
- বন্ধ
- ▁কর্মকর্তা
- চ্ছি
- ▁তথ্য
- ▁অংশ
- ▁দশ
- ▁তাহা
- মন্ত্রী
- ৃত
- ▁ঠিক
- ▁রাত
- ▁আসা
- ▁থানা
- ▁গোল
- রাজ
- ▁মৃত্যু
- ▁রি
- ▁পথ
- ্যান
- ▁বিচার
- ▁শ্রমিক
- ▁গল্প
- ▁সকাল
- ▁হাতে
- ▁এটা
- ▁কবি
- ▁বাবা
- ▁দাবি
- ▁চাই
- ▁মাধ্যমে
- ▁হয়েছিল
- ▁ঢ
- ▁যাচ্ছে
- ▁২০০
- ▁চলচ্চিত্র
- ▁রহমান
- ▁লেখা
- ▁দেন
- ▁পুরুষ
- চিত্র
- ▁ব্যবহার
- ▁অনুষ্ঠান
- ▁বর্তমান
- ▁ধর্ম
- ▁দাঁড়
- ▁নিহত
- ঃ
- চ্ছ
- ▁চেষ্টা
- ▁চোখ
- ▁উপজেলা
- ▁আদালত
- ▁সামনে
- ▁রু
- ▁চেয়ে
- ▁সর্ব
- ▁হত্যা
- ▁গণ
- ▁ডাক
- ▁দ্বিতীয়
- ▁ধরনের
- ▁কবিতা
- ▁ফলে
- ▁সবচেয়ে
- গুলি
- ▁মোট
- ▁পরিবার
- ▁শিশু
- ▁হোসেন
- ▁রেখে
- ▁রায়
- ▁মাথা
- ▁দুর্
- ▁৮
- ▁টা
- ▁৭
- ▁বসে
- ▁ওয়া
- ▁ব্যক্তি
- ▁শুধু
- ▁ব্যাংক
- ▁পাকিস্তান
- ▁যখন
- ▁করিয়া
- ▁লিখ
- পূর্ণ
- ▁বিশ্ববিদ্যালয়
- ▁সংখ্যা
- ▁যুদ্ধ
- ▁হইয়া
- ▁ক্ষমতা
- ▁সাধারণ
- ▁কোটি
- ▁শিক্ষা
- ▁আলো
- ▁তুলে
- ▁সত্য
- ▁ঘটে
- ''''
- ▁দূর
- ▁প্রশ্ন
- ুদ্ধ
- ▁লাখ
- ▁নিজের
- েশন
- ▁আলোচনা
- ঈ
- ▁ক্রিকেট
- ▁সমাজ
- ▁বয়স
- ▁গ্রহণ
- ▁জায়গা
- ▁ব্যবসা
- বর্তী
- জীব
- কল্প
- ▁প্রত্য
- ▁মাত্র
- ▁উৎ
- ▁শহরে
- ▁এখানে
- ▁নেয়া
- ▁ঘোষণা
- ▁সকল
- ▁আটক
- ▁নিরাপত্তা
- ▁পাঁচ
- ▁পূর্ব
- ▁রাষ্ট্র
- ▁ভাই
- ▁বহু
- ▁পরীক্ষা
- ▁পুরো
- ▁বাইরে
- ▁থাকবে
- ▁ক্ষেত্রে
- ▁স্থান
- ▁ম্যাচ
- ▁ঘরে
- ▁সবাই
- ার্ড
- ▁উদ্ধার
- ▁ইতিহাস
- ▁সাহিত্য
- ▁সুযোগ
- ▁আন্দোলন
- ▁যুক্তরাষ্ট্র
- দর্শন
- ▁১২
- ▁১৮
- ▁প্রেম
- ▁আন্তর্জাতিক
- ল্যান্ড
- ▁সমস্যা
- ▁বিভাগ
- ▁সিদ্ধান্ত
- ▁মধ্য
- ন্দি
- ▁ছাত্র
- ▁গাড়ি
- ▁দীর্ঘ
- ▁সংবাদ
- ▁প্রয়োজন
- ▁সিনেমা
- ▁রাজধানী
- ▁স্থানীয়
- ▁একটু
- ▁বাজার
- জ্জ
- ▁পৃথিবী
- ▁বিশ্বাস
- ▁আহত
- ▁দায়িত্ব
- ▁হরতাল
- ▁সম্ভব
- ▁অফিস
- ▁অভিনয়
- ▁কলেজ
- ▁চট্টগ্রাম
- ▁ক্ল
- ▁দক্ষিণ
- ▁পক্ষে
- ▁মুক্তি
- ▁সংসদ
- ‘
- ▁উপস্থিত
- ▁ফিরে
- ▁আগামী
- ▁সংগঠন
- ▁মিনিট
- ▁হামলা
- ▁প্রতিষ্ঠান
- ▁পোশাক
- ▁প্ল
- ▁সৃষ্টি
- ▁কমিশন
- ▁আমাকে
- ▁তদন্ত
- ▁উচ্চ
- ▁রাজনীতি
- দ্দ
- ▁দর্শক
- ▁তুমি
- ▁পরিস্থিতি
- াহার
- ▁ক্ষতি
- ▁আত্ম
- ▁গ্রেপ্তার
- ▁ফুট
- ▁পাশাপাশি
- মূল
- ▁প্রধানমন্ত্রী
- কর্মী
- ▁সুন্দর
- ▁নিয়ম
- ▁আগুন
- বিজ্ঞান
- ▁সাংবাদিক
- ▁লক্ষ্য
- ▁অবশ্য
- ▁শরীর
- ▁উল্লেখ
- ▁শতাংশ
- ▁স্কুল
- ভূত
- ▁গ্রন্থ
- ▁কখনো
- ▁প্রাণ
- ▁কারখানা
- ▁হিন্দু
- ▁বিবিসি
- ▁আপনার
- ▁আহমেদ
- ▁স্ত্রী
- বর্ষ
- ▁শক্তি
- সভা
- ▁রাস্তা
- ▁রকম
- ▁পশ্চিম
- ▁অপরাধ
- ▁আসছে
- ▁সংস্থা
- ▁পৌঁছ
- ▁দোকান
- ▁পত্রিকা
- ▁লেখক
- ▁সন্তান
- ▁ভেতর
- ▁এগিয়ে
- ▁নদী
- ▁হইল
- ▁পরিবেশ
- ▁প্রেসিডেন্ট
- ▁ছেড়ে
- ▁চেয়ারম্যান
- ▁ধারা
- বৃত্ত
- ▁বিক্রি
- ▁শ্রী
- ▁রক্ষা
- ▁দ্রুত
- ▁পরিচয়
- ▁মালিক
- ▁উপন্যাস
- ▁শিক্ষার্থী
- ▁অন্যতম
- ▁চরিত্র
- ▁প্রতিবেদন
- ▁প্রস্তুত
- ▁অভিযান
- তন্ত্র
- ▁অগ্নি
- ▁জনগণ
- ▁বৃহস্পতিবার
- ▁ব্যাপক
- ▁অনুযায়ী
- ▁পরিবর্তন
- ▁কলকাতা
- ভূমি
- ▁নজরুল
- ▁ভূমিকা
- ▁জনপ্রিয়
- ▁শিক্ষক
- ▁তেমন
- ▁অন্যান্য
- ▁বিদ্যুৎ
- খ্যাত
- ▁অস্ত্র
- ▁প্রস্তাব
- ▁স্বামী
- ▁পরিচিত
- ▁আয়োজন
- ▁শনিবার
- ▁তাঁকে
- ▁যাত্রী
- প্রাপ্ত
- ▁কর্মসূচি
- ▁গঠন
- ▁প্রভাব
- ▁কৃষ্ণ
- ▁সমাবেশ
- ▁সূত্র
- ▁অনুষ্ঠিত
- ▁পর্যায়ে
- ঋ
- ▁পুরস্কার
- ▁বিক্ষোভ
- ▁নিয়ন্ত্রণ
- ▁রোববার
- ▁প্রার্থী
- ▁যোগাযোগ
- ▁সোমবার
- ▁মার্চ
- ▁কমিটি
- ▁সংঘর্ষ
- ▁বুধবার
- ▁সামাজিক
- ▁তাঁদের
- ▁মার্কিন
- ▁সামরিক
- ▁নিজেদের
- ▁মঙ্গলবার
- ▁বক্তব্য
- ▁চুক্তি
- ▁যুগ
- ▁বৈঠক
- ▁ইউনিয়ন
- ▁মোহাম্মদ
- অ
- ▁তাঁহার
- ▁নির্মাণ
- ▁জানুয়ারি
- ▁আবেদন
- ▁বিশ্বকাপ
- ▁ফেব্রুয়ারি
- ▁তরুণ
- ▁হিসাব
- ▁সন্ধ্যা
- ▁পরিকল্পনা
- ▁উইকেট
- ▁ধারণা
- ▁আনন্দ
- মুক্ত
- ▁উদ্দেশ্য
- ▁চিকিৎসা
- ▁উন্নয়ন
- ▁আধুনিক
- ▁ভিত্তি
- ':'
- "\x94"
- ঢ
-
- ়
- e
- /
- i
- r
- t
- o
- '%'
- l
- a
- n
- '!'
- p
- '"'
- s
- '?'
- d
- '0'
- '3'
- u
- ঞ
- f
- g
- c
- m
- h
- –
- w
- b
- ;
- x
- '8'
- '5'
- '9'
- k
- ”
- y
- H
- L
- T
- j
- ৗ
- B
- K
- _
- z
- “
- F
- v
- '4'
- '1'
- '2'
- ঔ
- ঊ
- "\x93"
- D
- O
- œ
- ঐ
- ৰ
- —
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_bpe1000/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| a4794fa46c0afc1cb6bf78ea21694026 |
shripadbhat/whisper-large-v2-tt | shripadbhat | whisper | 15 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['tt'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,008 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large v2 Tatar
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 3e6e17bf76b6b99e830517e337fcc1dd |
anas-awadalla/gpt2-span-head-few-shot-k-64-finetuned-squad-seed-0 | anas-awadalla | gpt2 | 20 | 3 | transformers | 0 | question-answering | true | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 968 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-span-head-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
| 02ea580c5a0a47ca35e8f4aff9a2443e |
rayendito/mt5-small-finetuned-xl-sum-indonesia | rayendito | mt5 | 12 | 3 | transformers | 0 | summarization | true | false | false | apache-2.0 | null | ['xl_sum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization', 'generated_from_trainer'] | true | true | true | 912 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-xl-sum-indonesia
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xl_sum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
| ccbc1317855e2846e44633cd8d5d55e3 |
muhtasham/tiny-mlm-glue-cola-target-glue-qnli | muhtasham | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,806 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-cola-target-glue-qnli
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4706
- Accuracy: 0.7820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6037 | 0.15 | 500 | 0.5447 | 0.7315 |
| 0.5395 | 0.31 | 1000 | 0.5304 | 0.7417 |
| 0.5171 | 0.46 | 1500 | 0.4946 | 0.7626 |
| 0.5141 | 0.61 | 2000 | 0.5316 | 0.7450 |
| 0.5107 | 0.76 | 2500 | 0.4847 | 0.7712 |
| 0.5031 | 0.92 | 3000 | 0.4687 | 0.7844 |
| 0.4903 | 1.07 | 3500 | 0.4536 | 0.7897 |
| 0.48 | 1.22 | 4000 | 0.4689 | 0.7829 |
| 0.4677 | 1.37 | 4500 | 0.4769 | 0.7763 |
| 0.474 | 1.53 | 5000 | 0.4706 | 0.7820 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 90feab92a8a7356acdbe64c5dd90dffe |
ZakaryaRouzki/t5-punctuation | ZakaryaRouzki | t5 | 9 | 11 | transformers | 1 | text2text-generation | true | false | false | apache-2.0 | ['fr'] | ['orange_sum', 'mlsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['t5', 'french', 'punctuation'] | false | true | true | 1,603 | false |
# 🚀 Text Punctuator Based on Transformers model T5.
T5 model fine-tuned for punctuation restoration.
Model currently supports only French Language. More language supports will be added later using mT5.
Train Datasets :
Model trained using 2 french datasets (around 500k records):
- [orange_sum](https://huggingface.co/datasets/orange_sum)
- [mlsum](https://huggingface.co/datasets/mlsum) (only french text)
More info will be added later.
## 🚀 Usage
**TextPunctuator as a wrapper of the model.**
1. Install the package.
```bash
pip install TextPunctuator
```
2. Simple example
```python
from Punctuator import TextPunctuator
punctuator = TextPunctuator(use_gpu=False)
# text input
text = "Sur la base de ces échanges Blake Lemoine a donc jugé que le système avait atteint \
un niveau de conscience lui permettant d'être sensible Ce dernier a ensuite envoyé \
par email un rapport sur la sensibilité supposée de LaMDA à deux cents employés de \
Google Très vite les dirigeants de l’entreprise ont rejeté les allégations"
text_punctuated = punctuator.punctuate(text, lang='fr')
text_punctuated
# output :
""" Sur la base de ces échanges, Blake Lemoine a donc jugé que le système avait atteint un niveau de
conscience lui permettant d’être sensible. Ce dernier a ensuite envoyé par email un rapport sur
la sensibilité supposée de LaMDA à deux cents employés de Google. Très vite, les dirigeants de
l’entreprise ont rejeté les allégations. """
```
## ☕ Contact
Contact [Zakarya ROUZKI ](mailto:zakaryarouzki@gmail.com) or at [Linkedin](https://linkedin.com/in/rouzki).
| 4444649a6bb4ff2fed298a6d6e9290b7 |
jonatasgrosman/exp_w2v2t_th_xls-r_s625 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['th'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'th'] | false | true | true | 453 | false | # exp_w2v2t_th_xls-r_s625
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| b0f491eec05ba8fa49225d6df593e9cc |
jianleo/lora_ruhua_sd_1k | jianleo | null | 11 | 0 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | true | true | 457 | false |
# LoRA DreamBooth - jianleo/lora_ruhua_sd_1k
These are LoRA adaption weights for /root/autodl-tmp/sd_weights/models--runwayml--stable-diffusion-v1-5/snapshots/889b629140e71758e1e0006e355c331a5744b4bf. The weights were trained on a photo of rha woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
| 2eeb93e418686321eb84553e87ae31f8 |
MultiBertGunjanPatrick/multiberts-seed-0-140k | MultiBertGunjanPatrick | bert | 7 | 3 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | true | true | 6,483 | false | # MultiBERTs Seed 0 Checkpoint 140k (uncased)
Seed 0 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint.
The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
## Model description
MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the MultiBERTs model as inputs.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-140k')
model = BertModel.from_pretrained("multiberts-seed-0-140k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint.
## Training data
The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
of 256. The sequence length was set to 512 throughout. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-16163,
author = {Thibault Sellam and
Steve Yadlowsky and
Jason Wei and
Naomi Saphra and
Alexander D'Amour and
Tal Linzen and
Jasmijn Bastings and
Iulia Turc and
Jacob Eisenstein and
Dipanjan Das and
Ian Tenney and
Ellie Pavlick},
title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
journal = {CoRR},
volume = {abs/2106.16163},
year = {2021},
url = {https://arxiv.org/abs/2106.16163},
eprinttype = {arXiv},
eprint = {2106.16163},
timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=multiberts">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
| dffad5e605a7a72be0416f877f3862e0 |
thkkvui/xlm-roberta-base-finetuned-panx-en | thkkvui | xlm-roberta | 10 | 3 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,325 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4004
- F1: 0.6700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1798 | 1.0 | 50 | 0.6616 | 0.4612 |
| 0.5404 | 2.0 | 100 | 0.4206 | 0.6551 |
| 0.3714 | 3.0 | 150 | 0.4004 | 0.6700 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.13.0.dev20220711
- Datasets 2.4.0
- Tokenizers 0.12.1
| c292815ec404d2589201bd477d849eab |
stanfordnlp/stanza-wo | stanfordnlp | null | 9 | 9 | stanza | 0 | token-classification | false | false | false | apache-2.0 | ['wo'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stanza', 'token-classification'] | false | true | true | 578 | false | # Stanza model for Wolof (wo)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2022-09-25 02:14:15.533
| 1d973a2c48f8d6fc0084ee7319bb3ff6 |
Helsinki-NLP/opus-mt-eo-it | Helsinki-NLP | marian | 11 | 20 | transformers | 0 | translation | true | true | false | apache-2.0 | ['eo', 'it'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 1,999 | false |
### epo-ita
* source group: Esperanto
* target group: Italian
* OPUS readme: [epo-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ita/README.md)
* model: transformer-align
* source language(s): epo
* target language(s): ita
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.epo.ita | 23.8 | 0.465 |
### System Info:
- hf_name: epo-ita
- source_languages: epo
- target_languages: ita
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ita/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['eo', 'it']
- src_constituents: {'epo'}
- tgt_constituents: {'ita'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ita/opus-2020-06-16.test.txt
- src_alpha3: epo
- tgt_alpha3: ita
- short_pair: eo-it
- chrF2_score: 0.465
- bleu: 23.8
- brevity_penalty: 0.9420000000000001
- ref_len: 67118.0
- src_name: Esperanto
- tgt_name: Italian
- train_date: 2020-06-16
- src_alpha2: eo
- tgt_alpha2: it
- prefer_old: False
- long_pair: epo-ita
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | 3a88e8c784e77f5bdbaaf16f6199897f |
turkish-nlp-suite/tr_core_news_lg | turkish-nlp-suite | null | 26 | 10 | spacy | 0 | token-classification | false | false | false | cc-by-sa-4.0 | ['tr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['spacy', 'token-classification'] | false | true | true | 117,734 | false | Turkish large sized pipeline for TrSpaCy. Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner
| Feature | Description |
| --- | --- |
| **Name** | `tr_core_news_lg` |
| **Version** | `3.4.2` |
| **spaCy** | `>=3.4.2,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` |
| **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
| **Sources** | [UD Turkish BOUN](https://github.com/UniversalDependencies/UD_Turkish-BOUN) (Türk, Utku; Atmaca, Furkan; Özateş, Şaziye Betül; Berk, Gözde; Bedir, Seyyit Talha; Köksal, Abdullatif; Öztürk Başaran, Balkız; Güngör, Tunga; Özgür, Arzucan)<br />[Turkish Wiki NER dataset](https://github.com/turkish-nlp-suite/NER-datasets/tree/main/Turkish-Wiki-NER-Dataset) (Duygu Altinok, Co-one Istanbul)<br />[PANX/WikiANN](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji)<br />[Large-sized Turkish Floret word vectors (MC4 corpus)](https://huggingface.co/turkish-nlp-suite/tr_vectors_web_lg) (Duygu Altinok) |
| **License** | `cc-by-sa-4.0` |
| **Author** | [Duygu Altinok](https://github.com/turkish-nlp-suite/turkish-spacy-models) |
### Label Scheme
<details>
<summary>View label scheme (1552 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `ADP`, `ADV`, `ANum`, `ANum_Adj`, `ANum_Ness`, `ANum_Noun`, `ANum_With`, `ANum_Zero`, `Abr`, `Abr_With`, `Adj`, `Adj_Ness`, `Adj_With`, `Adj_Without`, `Adj_Zero`, `Adv`, `Adverb`, `Adverb_Adverb`, `Adverb_Noun`, `Adverb_Zero`, `Conj`, `Conj_Conj`, `DET`, `Demons`, `Demons_Zero`, `Det`, `Det_Zero`, `Dup`, `Interj`, `NAdj`, `NAdj_Aux`, `NAdj_Ness`, `NAdj_Noun`, `NAdj_Rel`, `NAdj_Verb`, `NAdj_With`, `NAdj_Without`, `NAdj_Zero`, `NNum`, `NNum_Rel`, `NNum_Zero`, `NOUN`, `Neg`, `Ness`, `Noun`, `Noun_Ness`, `Noun_Noun`, `Noun_Rel`, `Noun_Since`, `Noun_Verb`, `Noun_With`, `Noun_With_Ness`, `Noun_With_Verb`, `Noun_With_Zero`, `Noun_Without`, `Noun_Zero`, `PCAbl`, `PCAbl_Rel`, `PCAcc`, `PCDat`, `PCDat_Zero`, `PCGen`, `PCIns`, `PCIns_Zero`, `PCNom`, `PCNom_Adj`, `PCNom_Noun`, `PCNom_Zero`, `PRON`, `PUNCT`, `Pers`, `Pers_Ness`, `Pers_Pers`, `Pers_Rel`, `Pers_Zero`, `Postp`, `Prop`, `Prop_Conj`, `Prop_Rel`, `Prop_Since`, `Prop_With`, `Prop_Zero`, `Punc`, `Punc_Noun_Ness`, `Punc_Noun_Rel`, `Quant`, `Quant_Zero`, `Ques`, `Ques_Zero`, `Reflex`, `Reflex_Zero`, `Rel`, `SYM`, `Since`, `Since_Since`, `Verb`, `Verb_Conj`, `Verb_Ness`, `Verb_Noun`, `Verb_Verb`, `Verb_With`, `Verb_Zero`, `With`, `Without`, `Without_Zero`, `Zero` |
| **`morphologizer`** | `NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `POS=PUNCT`, `POS=ADV`, `POS=NOUN`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3`, `POS=DET`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3`, `POS=ADJ`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `POS=PRON`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Dat\|Number=Sing\|POS=PROPN\|Person=3`, `POS=VERB\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `POS=INTJ`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Acc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3`, `POS=CCONJ`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=ADP\|Person=3`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|POS=VERB\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `POS=ADP`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=1`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Sing\|POS=NOUN\|Person=3`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Loc\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=1`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Loc\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Des,Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Ins\|POS=VERB\|Polarity=Neg`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=ADJ\|Person=2,3\|Polarity=Pos`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Nom\|POS=NOUN\|Polarity=Pos`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=3`, `Case=Loc\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `POS=VERB\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `POS=AUX`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=NUM\|Person=3`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Abbr=Yes\|Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Mood=Pot\|POS=VERB\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `POS=VERB`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Gen\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Rfl`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Equ\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=1`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|NumType=Ord\|Number=Sing\|POS=NUM\|Person=3`, `Case=Nom\|Number=Sing\|POS=AUX\|Person=3`, `Case=Nom\|Number=Sing\|POS=ADV\|Person=3`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=3`, `Case=Ins\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur,Sing\|POS=NOUN\|Person=2,3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1,3\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Conv`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|POS=ADV\|Polarity=Pos`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=1`, `POS=PROPN`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Loc\|POS=VERB\|Polarity=Pos`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Loc\|Number=Sing\|POS=ADJ\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Aspect=Perf\|Number[psor]=Sing\|POS=AUX\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=2\|Person[psor]=3`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=2`, `POS=VERB\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos\|Tense=Pres`, `Number=Sing\|POS=VERB\|Person=3`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=ADJ\|Person=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Mood=Imp\|POS=VERB\|VerbForm=Conv`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Number=Sing\|POS=AUX\|Person=3`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Abl\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Case=Loc\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=2`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Case=Nom\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=2`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `POS=NUM`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur\|POS=PRON\|Person=1,3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=ADJ\|Person=3`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|POS=VERB\|Polarity=Neg`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Abl\|POS=VERB\|Polarity=Pos`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `NumType=Ord\|POS=NUM`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Loc,Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `POS=SYM`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Number=Plur\|POS=VERB\|Person=1`, `Case=Dat\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=PRON\|Person=1,3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=ADP\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=3\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Imp\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=PRON\|Person=2,3\|Polarity=Pos\|PronType=Dem`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Evident=Nfh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Case=Loc\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Abl\|POS=VERB\|Polarity=Pos\|Voice=Pass`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Equ\|Number=Sing\|POS=NUM\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Hab\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Conv`, `Case=Ins\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Pot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Dat\|Number=Plur\|POS=AUX\|Person=3`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Echo=Rdp\|POS=X`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Abl\|Number=Plur\|POS=PROPN\|Person=3`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Equ\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Ins\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Ins\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Conv`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Evident=Nfh\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Conv`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=3`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NUM\|Person=1\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Ins\|POS=VERB\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=PROPN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `POS=NOUN\|Polarity=Pos`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Imp,Perf\|Mood=Gen\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Abl\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=1\|Person[psor]=2`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Abbr=Yes\|Case=Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Ins\|Number=Plur\|POS=NUM\|Person=3`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Abl\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Prog\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Abl\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Mood=Pot\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Hab,Perf\|Mood=Cnd,Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|POS=VERB\|Polarity=Neg\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=1`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab,Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|Polarity=Neg\|Tense=Past,Pres\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Evident=Nfh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Imp\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Hab\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Case=Nom\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=ADP\|Person=3\|Tense=Pres`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Rfl`, `Case=Acc\|Number=Sing\|POS=ADP\|Person=3`, `Case=Loc,Nom\|Number=Sing\|POS=PRON\|Person=3`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp,Perf\|Mood=Gen\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut,Pres`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `POS=VERB\|Polarity=Pos\|Voice=Rfl`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Number=Sing\|POS=VERB\|Person=1`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=NUM\|Person=3`, `Case=Ins\|Number=Plur\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=1\|Person[psor]=3\|Tense=Past`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Number=Sing\|POS=ADP\|Person=3`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|POS=VERB\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pqp`, `Aspect=Perf\|Mood=Ind\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3\|Tense=Past`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=ADJ\|Polarity=Pos`, `Aspect=Imp\|Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Acc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Pres`, `POS=PROPN\|Polarity=Pos`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Cau`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|POS=ADP\|Person=3`, `Aspect=Perf\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Dat,Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person=3\|Person[psor]=3\|Tense=Pres`, `Evident=Nfh\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Prog\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=3\|Polarity=Pos`, `Case=Loc\|Number=Plur\|POS=ADJ\|Person=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Aspect=Imp\|Case=Dat\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=2,3\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Equ\|Number=Sing\|POS=ADJ\|Person=3`, `Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Neg`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Ins\|Number=Sing\|POS=ADJ\|Person=3`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Evident=Nfh\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Plur\|POS=ADJ\|Person=3`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Abl\|Number=Plur\|POS=VERB\|Person=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Loc\|POS=NOUN\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Gen\|Evident=Fh\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|Tense=Past`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Rcp`, `POS=ADV\|Polarity=Pos`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Rcp`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos`, `Aspect=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut`, `Aspect=Hab\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Case=Nom\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Reflex=Yes`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Rfl`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=ADP\|Person=3\|Tense=Past`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Mood=Imp\|Number=Sing\|POS=ADJ\|Person=2\|Polarity=Pos`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=1`, `Case=Acc\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Pres`, `Case=Abl\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Dat\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2\|Reflex=Yes`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Plur,Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Gen\|Number=Plur\|POS=ADJ\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=PROPN\|Person=1,3\|Tense=Past`, `Abbr=Yes\|Case=Dat\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=2`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=1`, `Evident=Nfh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `POS=SCONJ`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Acc\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Perf\|Case=Gen\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Dat\|Number=Plur\|POS=ADP\|Person=3`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `NumType=Dist\|POS=NUM`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PART\|Person=3\|Person[psor]=3`, `POS=ADP\|Polarity=Pos`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Plur\|POS=PROPN\|Person=3`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1,3`, `Case=Equ\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Perf\|Case=Loc\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=2\|Voice=Rfl`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Conv`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Loc,Nom\|Number=Plur,Sing\|POS=NOUN\|Person=2,3`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=1`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=X\|Person=3`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Gen\|Mood=Gen\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Person[psor]=1`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Aspect=Prog\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=NUM\|Person=3`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Ins\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Mood=Imp\|Number=Sing\|POS=NOUN\|Person=2,3\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Hab\|Evident=Nfh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Loc\|POS=VERB\|Polarity=Neg`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Mood=Imp\|Number=Plur,Sing\|POS=ADJ\|Person=2,3\|Polarity=Pos`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Aspect=Prog\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1\|Tense=Past`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Evident=Nfh\|Mood=Gen\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past,Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Evident=Nfh\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Gen,Pot\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `NumType=Card\|POS=ADJ`, `Case=Gen,Nom\|Number=Plur,Sing\|POS=PRON\|Person=1,3`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Acc\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|Person[psor]=2`, `Case=Ins\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Acc\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Hab\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Case=Ins\|POS=VERB\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=2`, `Case=Nom\|Number=Plur\|POS=NUM\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Person[psor]=3`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADP\|Person=1\|Tense=Pres`, `Aspect=Hab\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Prog\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Rfl`, `Case=Nom\|Number=Plur,Sing\|POS=ADJ\|Person=2,3`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Imp\|Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Aspect=Hab\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Voice=Cau`, `Case=Equ\|Number=Plur\|POS=NUM\|Person=3`, `Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Number=Sing\|POS=VERB\|Person=2`, `Aspect=Imp\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1`, `Number=Sing\|POS=ADJ\|Person=1`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADP\|Person=1\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=X\|Person=3\|Person[psor]=1`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=1\|Person[psor]=3`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Ind,Nec\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Polarity=Pos\|Tense=Past`, `Mood=Nec\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Number=Sing\|POS=ADV\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Case=Loc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Imp\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab,Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number[psor]=Sing\|POS=VERB\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Prog\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Polite=Infm\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=2\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Loc\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2`, `Case=Abl\|Number=Sing\|POS=NOUN\|Person=2`, `Case=Equ\|Number=Plur\|POS=NOUN\|Person=3`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Rfl`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Cnd\|Number=Sing\|POS=PRON\|Person=1,3\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Rfl`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Aspect=Perf\|Case=Acc\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=2`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADP\|Person=3\|Person[psor]=2`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Imp\|Number[psor]=Sing\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut`, `Case=Nom\|POS=VERB\|Polarity=Neg\|Voice=Pass`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Abl\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Hab\|Case=Nom\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Evident=Nfh\|Mood=Gen\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past,Pres`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=3`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Hab\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=1\|Person[psor]=1`, `Aspect=Imp\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=ADJ\|Person=1,3\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=NOUN\|Person=2`, `Case=Loc,Nom\|Number=Plur,Sing\|POS=PRON\|Person=1,3`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Abl\|Mood=Pot\|POS=VERB\|Polarity=Pos`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Evident=Nfh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Prog\|Case=Nom\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Number=Plur\|POS=ADJ\|Person=1`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Hab,Perf\|Mood=Gen\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `POS=X`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Conv`, `Aspect=Hab\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Mood=Imp\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Rfl`, `Case=Abl\|POS=VERB\|Polarity=Neg`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=DET\|Person=3\|Tense=Past`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=2,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|Voice=Cau`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=ADP\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Loc\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3\|Tense=Pres`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Cau`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Case=Loc,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=3`, `Evident=Nfh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Nom\|Mood=Cnd\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=3\|Person[psor]=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Mood=Des\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Past`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Number=Plur\|POS=NOUN\|Person=1`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=1`, `Aspect=Imp\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut`, `Case=Dat\|NumType=Card\|Number=Sing\|POS=NUM\|Person=3`, `Aspect=Prog\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Abl\|Number=Plur\|POS=NOUN\|Person=1`, `Case=Equ\|Number=Sing\|POS=VERB\|Person=3`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp,Perf\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Pass`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Case=Nom\|Mood=Des\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Voice=Cau`, `Aspect=Hab\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Aspect=Imp\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Cau`, `Case=Nom\|Number=Plur\|POS=ADJ\|Person=3\|Polarity=Pos`, `Number=Plur\|POS=NOUN\|Person=2`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=ADP\|Person=2\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Des\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|Voice=Cau`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=ADJ\|Person=1,3\|Tense=Past`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Mood=Pot\|POS=VERB\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Mood=Gen,Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2`, `Case=Loc,Nom\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Hab\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Case=Loc\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past`, `Case=Nom\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Voice=Cau`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Abl,Loc\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Tense=Pres`, `Aspect=Perf\|Case=Nom\|Mood=Gen\|Number=Plur,Sing\|POS=PRON\|Person=3\|Tense=Pres`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=2`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|Voice=Cau`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=1`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Perf\|Evident=Fh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|Tense=Past`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NOUN\|Person=3`, `Case=Nom\|Number=Plur\|POS=AUX\|Person=1`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|POS=NOUN\|Person=1,3\|Tense=Pres`, `Aspect=Imp\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Case=Abl\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=3\|Polarity=Neg\|Tense=Pres`, `Aspect=Perf\|Evident=Fh\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=2`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=2\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=ADP\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Pass`, `Abbr=Yes\|Case=Loc\|Number=Sing\|POS=PROPN\|Person=3`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2`, `Aspect=Perf\|Number[psor]=Sing\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=2`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Vnoun`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Aspect=Hab,Perf\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=1\|Person[psor]=1`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Aspect=Perf\|Mood=Gen\|Number=Sing\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Past,Pres\|VerbForm=Part`, `Case=Equ\|Number=Sing\|POS=PROPN\|Person=3`, `Aspect=Perf\|Case=Nom\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=2,3\|Tense=Past`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Imp\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Part`, `Case=Loc,Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1`, `Aspect=Hab\|Case=Nom\|Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos\|Tense=Pres`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person=2\|Person[psor]=1`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Nom\|Mood=Nec\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg`, `Case=Ins\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres`, `Case=Equ\|Number=Sing\|Number[psor]=Sing\|POS=ADP\|Person=3\|Person[psor]=3`, `Case=Loc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Evident=Nfh\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Prog\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Aspect=Perf\|Number[psor]=Plur\|POS=VERB\|Person[psor]=3\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Aspect=Perf\|Case=Loc\|Mood=Gen\|Number=Plur,Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Aspect=Perf\|Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Voice=Cau`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=1`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1\|Person[psor]=3`, `Aspect=Prog\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person=1\|Person[psor]=1`, `Aspect=Imp\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=1\|Polarity=Neg`, `Number=Sing\|POS=NOUN\|Person=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=AUX\|Person=3\|Person[psor]=3\|Polarity=Pos`, `Mood=Des\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Voice=Pass`, `Aspect=Perf\|Evident=Nfh\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=2`, `Aspect=Hab\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|Voice=Pass`, `POS=ADJ\|Polarity=Neg`, `Aspect=Perf\|Mood=Pot\|Number[psor]=Plur\|POS=VERB\|Person[psor]=1\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=2\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Nom\|Mood=Ind\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `Aspect=Prog\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Aspect=Imp,Perf\|Case=Nom\|Mood=Gen,Pot\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut,Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person=3\|Person[psor]=3`, `Aspect=Perf\|Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Cnd\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres`, `Case=Nom\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Nfh\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Aspect=Imp,Perf\|Mood=Cnd\|Number=Plur,Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut,Pres`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person=3\|Person[psor]=1\|Polarity=Pos`, `Mood=Pot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Cau`, `Aspect=Perf\|Case=Gen\|Mood=Cnd\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Voice=Cau`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Voice=Cau`, `Case=Loc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2`, `Aspect=Imp\|Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Part`, `Aspect=Perf\|Evident=Fh\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|Voice=Pass`, `Aspect=Hab\|Evident=Nfh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person=3\|Person[psor]=3`, `Case=Nom\|Evident=Nfh\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past`, `Case=Acc\|Number=Sing\|POS=NOUN\|Person=3\|Polarity=Pos`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Neg`, `Aspect=Imp\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `aux`, `aux:q`, `case`, `cc`, `cc:preconj`, `ccomp`, `clf`, `compound`, `compound:lvc`, `compound:redup`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `flat`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 91.19 |
| `POS_ACC` | 90.68 |
| `MORPH_ACC` | 89.13 |
| `LEMMA_ACC` | 82.32 |
| `DEP_UAS` | 73.48 |
| `DEP_LAS` | 63.73 |
| `SENTS_P` | 87.17 |
| `SENTS_R` | 81.92 |
| `SENTS_F` | 84.47 |
| `ENTS_F` | 88.90 |
| `ENTS_P` | 89.54 |
| `ENTS_R` | 88.28 | | c1a054076c216749166a22ef5fc634f6 |
amitkayal/ak-vit-base-patch16-224-in21k-image_classification | amitkayal | vit | 10 | 13 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['image_folder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,484 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ak-vit-base-patch16-224-in21k-image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1599
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.191 | 0.99 | 65 | 3.1599 | 1.0 |
| 2.7393 | 1.99 | 130 | 2.7834 | 1.0 |
| 2.5853 | 2.99 | 195 | 2.6595 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| f4dc94f46d6fb56c8feb4a2f3bb9ade2 |
stanfordnlp/stanza-pt | stanfordnlp | null | 17 | 235 | stanza | 2 | token-classification | false | false | false | apache-2.0 | ['pt'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stanza', 'token-classification'] | false | true | true | 583 | false | # Stanza model for Portuguese (pt)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2022-10-07 02:01:55.440
| c63f81a593ea165de93934e8e48503ef |
plasmo/naturitize-sd1-5-768px | plasmo | null | 22 | 109 | diffusers | 4 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 608 | false | ### Jak's **Naturitize** Image Pack (SD 1.5) for Stable Diffusion
--------------------
Another Jak Texture Pack Release is here to help create your earthy, creations!
Trained using 112 (768px) training images, 8000 training steps, 500 Text_Encoder_steps.
Use Prompt: "**naturitize**" in the beginning of your prompt followed by a word. *No major prompt-crafting needed*.
Thanks to /u/Jak_TheAI_Artist and /u/okamiueru for creating training images!
Sample pictures of this concept:
.jpg)
| 2c709d6655de9bbfadd28047b0e08144 |
tomekkorbak/hungry_carson | tomekkorbak | gpt2 | 137 | 0 | transformers | 0 | null | true | false | false | mit | ['en'] | ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 8,582 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hungry_carson
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 2362
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 2990407680},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '5c64636da035c40bb8b1186648a39822071476cb'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/cranky_lichterman'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'hungry_carson',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 251,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 2990407680,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1s8q2lyx | d88366d9240940415d92a537f73d6113 |
Langboat/mengzi-oscar-base-retrieval | Langboat | bert | 7 | 5 | transformers | 3 | fill-mask | true | false | false | apache-2.0 | ['zh'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,109 | false | # Mengzi-oscar-base-retrieval (Chinese Image-text retrieval model)
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
Mengzi-oscar-base-retrieval is fine-tuned based on Chinese multi-modal pre-training model [Mengzi-Oscar](https://github.com/Langboat/Mengzi/blob/main/Mengzi-Oscar.md), on COCO-ir dataset.
## Usage
#### Installation
Check [INSTALL.md](https://github.com/microsoft/Oscar/blob/master/INSTALL.md) for installation instructions.
#### Pretrain & fine-tune
See the [Mengzi-Oscar.md](https://github.com/Langboat/Mengzi/blob/main/Mengzi-Oscar.md) for details.
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
```
@misc{zhang2021mengzi,
title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese},
author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
year={2021},
eprint={2110.06696},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 8157b70c37c86a4f79f9b307db8c625e |
edwardjross/xlm-roberta-base-finetuned-panx-it | edwardjross | xlm-roberta | 10 | 8 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,313 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2532
- F1: 0.8331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6951 | 1.0 | 105 | 0.2967 | 0.7682 |
| 0.2824 | 2.0 | 210 | 0.2569 | 0.8201 |
| 0.1724 | 3.0 | 315 | 0.2532 | 0.8331 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 8e8b56822c0a83fac72ea4842447c313 |
malteos/gpt2-wechsel-german-ds-meg | malteos | gpt2 | 9 | 118 | transformers | 0 | text-generation | true | false | false | mit | ['de'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 649 | false |
# Replication of [gpt2-wechsel-german](https://huggingface.co/benjamin/gpt2-wechsel-german)
- trained with [BigScience's DeepSpeed-Megatron-LM code base](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- 22hrs on 4xA100 GPUs (~ 80 TFLOPs / GPU)
- stopped after 100k steps
- less than a single epoch on `oscar_unshuffled_deduplicated_de` (excluding validation set; original model was trained for 75 epochs on less data)
- bf16
- zero stage 1
- tp/pp = 1
## Evaluation
| Model | PPL |
|---|---|
| `gpt2-wechsel-german-ds-meg` | **26.4** |
| `gpt2-wechsel-german` | 26.8 |
| `gpt2` (retrained from scratch) | 27.63 |
## License
MIT
| e37c0ccfa7f47b8290b4b3ad54262347 |
espnet/aishell2_att_ctc_espnet2 | espnet | null | 33 | 8 | espnet | 0 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['zh'] | ['aishell2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | true | true | 28,764 | false |
## ESPnet2 ASR model
### `espnet/aishell2_att_ctc_espnet2`
This model was trained by jctian98 using aishell2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 04803559d6dcde718638cfbd98139a9ddad1da72
pip install -e .
cd egs2/aishell2/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/aishell2_att_ctc_espnet2
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Jun 16 16:51:22 CST 2022`
- python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]`
- espnet version: `espnet 202205`
- pytorch version: `pytorch 1.7.0`
- Git hash: `991eaa4a9e22c114ca59ef3988b4fcd0cdf25cdf`
- Commit date: `Sat Jun 11 14:09:32 2022 +0800`
## asr_train_asr_conformer_raw_zh_char_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_rnn_asr_model_valid.acc.ave/dev_ios|2500|2500|66.3|33.7|0.0|0.0|33.7|33.7|
|decode_asr_rnn_asr_model_valid.acc.ave/test_android|5000|5002|63.8|36.2|0.0|0.0|36.2|36.2|
|decode_asr_rnn_asr_model_valid.acc.ave/test_ios|5000|5002|65.5|34.5|0.0|0.0|34.5|34.5|
|decode_asr_rnn_asr_model_valid.acc.ave/test_mic|5000|5002|63.4|36.6|0.0|0.0|36.6|36.6|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/dev_ios|2500|2500|68.4|31.6|0.0|0.0|31.6|31.6|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_android|5000|5002|65.0|35.0|0.0|0.0|35.0|35.0|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_ios|5000|5002|66.5|33.4|0.0|0.0|33.5|33.4|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_mic|5000|5002|65.4|34.6|0.0|0.0|34.6|34.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_rnn_asr_model_valid.acc.ave/dev_ios|2500|24802|94.8|5.0|0.2|0.1|5.4|33.7|
|decode_asr_rnn_asr_model_valid.acc.ave/test_android|5000|49534|94.0|5.8|0.2|0.1|6.1|36.2|
|decode_asr_rnn_asr_model_valid.acc.ave/test_ios|5000|49534|94.5|5.4|0.2|0.1|5.7|34.5|
|decode_asr_rnn_asr_model_valid.acc.ave/test_mic|5000|49534|94.0|5.8|0.2|0.1|6.1|36.6|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/dev_ios|2500|24802|94.9|4.9|0.3|0.1|5.2|31.6|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_android|5000|49534|94.1|5.6|0.3|0.1|6.0|35.0|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_ios|5000|49534|94.6|5.1|0.2|0.1|5.5|33.4|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test_mic|5000|49534|94.3|5.5|0.2|0.1|5.8|34.6|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_zh_char_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 8
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37023
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- acc
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 20000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_zh_char_sp/train/speech_shape
- exp/asr_stats_raw_zh_char_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_zh_char_sp/valid/speech_shape
- exp/asr_stats_raw_zh_char_sp/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 51200
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_noeng_sp/wav.scp
- speech
- sound
- - dump/raw/train_noeng_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_ios/wav.scp
- speech
- sound
- - dump/raw/dev_ios/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0025
scheduler: warmuplr
scheduler_conf:
warmup_steps: 30000
token_list:
- <blank>
- <unk>
- 的
- 一
- 十
- 二
- 三
- 有
- 我
- 在
- 度
- 五
- 是
- 四
- 人
- 六
- 七
- 八
- 九
- 中
- 百
- 不
- 了
- 零
- 大
- 到
- 为
- 开
- 上
- 国
- 调
- 市
- 点
- 业
- 歌
- 么
- 来
- 个
- 这
- 年
- 要
- 公
- 什
- 会
- 出
- 地
- 发
- 行
- 能
- 温
- 电
- 空
- 万
- 千
- 成
- 和
- 分
- 时
- 下
- 你
- 场
- 新
- 家
- 打
- 产
- 机
- 对
- 以
- 房
- 生
- 把
- 小
- 首
- 放
- 之
- 现
- 日
- 动
- 高
- 子
- 后
- 多
- 们
- 者
- 方
- 前
- 也
- 他
- 视
- 资
- 将
- 关
- 金
- 天
- 于
- 进
- 过
- 经
- 听
- 月
- 可
- 用
- 自
- 最
- 司
- 幺
- 车
- 比
- 体
- 手
- 目
- 化
- 道
- 作
- 部
- 被
- 给
- 报
- 加
- 就
- 第
- 全
- 乐
- 定
- 得
- 还
- 事
- 城
- 本
- 想
- 女
- 赛
- 面
- 工
- 设
- 都
- 音
- 力
- 品
- 理
- 保
- 记
- 心
- 好
- 而
- 企
- 法
- 实
- 帮
- 价
- 长
- 看
- 合
- 已
- 海
- 但
- 与
- 名
- 北
- 同
- 入
- 元
- 商
- 通
- 量
- 区
- 学
- 情
- 京
- 网
- 所
- 务
- 主
- 说
- 两
- 政
- 播
- 利
- 重
- 制
- 员
- 平
- 其
- 交
- 内
- 风
- 提
- 器
- 间
- 没
- 请
- 去
- 相
- 台
- 美
- 期
- 增
- 明
- 信
- 式
- 次
- 爱
- 曲
- 建
- 安
- 当
- 管
- 表
- 东
- 店
- 里
- 起
- 并
- 从
- 果
- 回
- 民
- 影
- 展
- 据
- 着
- 示
- 更
- 等
- 应
- 很
- 无
- 门
- 外
- 数
- 运
- 因
- 投
- 正
- 今
- 收
- 路
- 些
- 需
- 儿
- 性
- 南
- 计
- 色
- 如
- 然
- 世
- 亿
- 物
- 光
- 项
- 特
- 联
- 智
- 持
- 随
- 向
- 搜
- 老
- 西
- 位
- 院
- 模
- 规
- 身
- 气
- 消
- 达
- 意
- 切
- 男
- 队
- 斯
- 米
- 低
- 格
- 水
- 张
- 此
- 布
- 灯
- 华
- 那
- 住
- 步
- 集
- 受
- 基
- 换
- 整
- 险
- 科
- 续
- 让
- 线
- 广
- 股
- 求
- 转
- 强
- 演
- 件
- 息
- 费
- 变
- 做
- 样
- 该
- 未
- 近
- 她
- 系
- 至
- 代
- 技
- 查
- 证
- 少
- 接
- 山
- 统
- 楼
- 节
- 标
- 只
- 战
- 及
- 文
- 总
- 王
- 局
- 己
- 再
- 问
- 监
- 处
- 传
- 服
- 州
- 显
- 销
- 快
- 由
- 频
- 改
- 便
- 卫
- 题
- 购
- 林
- 告
- 创
- 限
- 售
- 讯
- 常
- 界
- 营
- 原
- 单
- 超
- 认
- 种
- 流
- 亮
- 净
- 排
- 案
- 知
- 推
- 降
- 环
- 获
- 程
- 走
- 友
- 源
- 立
- 马
- 客
- 称
- 速
- 剧
- 周
- 决
- 尔
- 别
- 跑
- 取
- 完
- 片
- 警
- 头
- 球
- 选
- 士
- 级
- 拉
- 解
- 策
- 结
- 术
- 约
- 银
- 江
- 星
- 活
- 口
- 直
- 备
- 支
- 供
- 户
- 医
- 存
- 花
- 易
- 各
- 造
- 置
- 准
- 任
- 非
- 红
- 游
- 专
- 较
- 款
- 预
- 积
- 站
- 园
- 升
- 先
- 牌
- 社
- 办
- 每
- 李
- 村
- 型
- 使
- 难
- 势
- 真
- 带
- 指
- 停
- 构
- 导
- 深
- 唱
- 参
- 清
- 见
- 龙
- 研
- 团
- 照
- 确
- 阳
- 响
- 太
- 亚
- 克
- 闭
- 火
- 央
- 微
- 感
- 组
- 减
- 或
- 委
- 领
- 军
- 率
- 伤
- 始
- 类
- 书
- 融
- 具
- 济
- 土
- 施
- 望
- 教
- 奥
- 吗
- 际
- 育
- 权
- 涨
- 德
- 几
- 控
- 师
- 热
- 死
- 共
- 则
- 话
- 汽
- 许
- 份
- 府
- 居
- 态
- 连
- 黄
- 白
- 烦
- 引
- 英
- 声
- 狐
- 何
- 划
- 除
- 媒
- 季
- 继
- 孩
- 眼
- 财
- 岁
- 买
- 越
- 健
- 责
- 卡
- 助
- 索
- 宝
- 负
- 镇
- 争
- 松
- 况
- 半
- 条
- 税
- 注
- 校
- 终
- 仅
- 刘
- 某
- 号
- 福
- 才
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- 搔
- 瞑
- 祀
- 徜
- 恻
- 蟾
- 蹩
- 蕨
- 酰
- 薏
- 绫
- 濂
- 茛
- 囱
- 鲑
- 粑
- 鳗
- 札
- 觐
- 醍
- 掸
- 逑
- 阖
- 菖
- 嗲
- 幡
- 缙
- 逵
- 蔫
- 崧
- 惚
- 铰
- 嫔
- 倌
- 罡
- 邝
- 婀
- 纨
- 绔
- 嵘
- 孛
- 铣
- 娠
- 槿
- 厩
- 犷
- 朐
- 疝
- 狈
- 黍
- 幄
- 荚
- 淖
- 犊
- 塬
- 艮
- 胱
- 蝗
- 圪
- 擘
- 旮
- 旯
- 憩
- 孺
- 瞟
- 啵
- 焘
- 嗣
- 忿
- 嬗
- 蘸
- 纫
- 喟
- 慵
- 祟
- 踺
- 孳
- 棣
- 埸
- 淦
- 炔
- 纰
- 轫
- 偕
- 奘
- 纣
- 孀
- 舷
- 羌
- 圻
- 拈
- 鲅
- 镌
- 恃
- 骛
- 旻
- 煨
- 婊
- 雉
- 蔻
- 霈
- 垚
- 铩
- 莪
- 揩
- 枰
- 痢
- 庹
- 瘴
- 钎
- 腮
- 嵬
- 谯
- 嫡
- 埂
- 捋
- 纾
- 蛤
- 瑭
- 螈
- 邙
- 罔
- 郯
- 樵
- 茌
- 郓
- 枳
- 咦
- 讴
- 厝
- 砼
- 茯
- 衲
- 潋
- 噘
- 谚
- 烷
- 斡
- 嫫
- 嗪
- 邳
- 铄
- 歼
- 堇
- 渑
- 疽
- 怄
- 涣
- 囹
- 稔
- 弋
- 篝
- 蹋
- 窠
- 谟
- 浠
- 悱
- 蜍
- 孬
- 芥
- 馏
- 屐
- 栎
- 玷
- 萸
- 扞
- 阡
- 荀
- 曳
- 邕
- 诛
- 阉
- 堀
- 骠
- 琤
- 盂
- 妲
- 虻
- 醐
- 谀
- 舀
- 鳟
- 绀
- 呲
- 娩
- 牾
- 僮
- 笳
- 渥
- 仡
- 镊
- 嶝
- 泱
- 汴
- 咣
- 嘭
- 锟
- 咂
- 宓
- 侗
- 洹
- 妫
- Ａ
- 峁
- 蜊
- 攫
- 膑
- 毂
- 秣
- 泠
- 尅
- 冼
- 嶂
- 浈
- 陬
- 啖
- 兖
- 褴
- 褛
- 妯
- 娌
- 恣
- 恸
- 掬
- 篦
- 蹚
- 逡
- 鲷
- 叵
- 驷
- 飧
- 釉
- 粼
- 踯
- 躅
- 讷
- 吮
- 琮
- 啾
- 粲
- 佻
- 疸
- 臊
- 蓦
- 椋
- 眬
- 憷
- 绋
- 珙
- 揶
- 谏
- 䶮
- 帛
- 衮
- 晷
- 裨
- 鸾
- 槎
- 讣
- 嫚
- 遨
- 瘙
- 疱
- 呻
- 鞅
- 痉
- 挛
- 骰
- 瘳
- 棂
- 偃
- 鸢
- 钲
- 尕
- 呸
- 埇
- 浃
- 濯
- 坩
- 埚
- 嗝
- 炀
- 隗
- 扈
- 谆
- 丕
- 魉
- 噙
- 圹
- 埕
- 恪
- 孱
- 凛
- 曜
- 拚
- 浔
- 吖
- 轱
- 搽
- 芪
- 箕
- 箔
- 戊
- 蛆
- 蜱
- 嗔
- 榛
- 蹒
- 跚
- 镣
- 鲫
- 镂
- 摈
- 愫
- 纂
- 麝
- 趺
- 碜
- 馁
- 唷
- 悻
- 伫
- 樾
- 剜
- 咝
- 銮
- 撺
- 掇
- 哂
- 咻
- 酐
- 訾
- 鳕
- 稷
- 嘣
- 碣
- 扦
- 柩
- 蟠
- 芩
- 鬓
- 裱
- 嗄
- 枋
- 钇
- 怼
- 喁
- 龊
- 疴
- 蛹
- 偓
- 蓼
- 汩
- 疖
- 蛎
- 诘
- 焓
- 荠
- 闩
- 噌
- 苷
- 藓
- 蚱
- 亘
- 缎
- 鼬
- 籼
- 疣
- 轸
- 玹
- 潺
- 妪
- 馀
- 啶
- 耄
- 耋
- 鬃
- 滹
- 莅
- 倜
- 傥
- 蓁
- 岬
- 貉
- 獾
- 敝
- 瘁
- 蒯
- 碓
- 殚
- 漭
- 嵛
- 榉
- 诓
- 泖
- 艋
- 凇
- 靑
- 沏
- 磴
- 氪
- 诲
- 忪
- 炷
- 杓
- 暾
- 藿
- Ｔ
- Ｍ
- 洺
- 擢
- 藠
- 晌
- 瞠
- 桁
- 遑
- 囗
- 谑
- 嗬
- 卲
- 硒
- 鼾
- 觥
- 茳
- 枇
- 杷
- 邡
- 桷
- 椁
- 鹳
- 饴
- 跶
- 绉
- 浐
- 迩
- 啲
- 颌
- 泺
- 睑
- 踮
- 荛
- 镔
- 祢
- 韫
- 笸
- 俎
- 羸
- 怿
- 昝
- 艿
- 薷
- 赅
- 怆
- 刍
- 獭
- 蚴
- 噶
- 噤
- 氤
- 氲
- 豺
- 倭
- 豉
- 葺
- 珥
- 痨
- 蹁
- 跹
- 蚬
- 唳
- 舐
- 竽
- 馑
- 徇
- 垌
- 魍
- 葚
- 涑
- 跛
- 荏
- 吋
- 髌
- 髂
- 骓
- 悌
- 戌
- 揄
- 矽
- 钒
- 𫖯
- 谶
- 捌
- 矍
- 铧
- 骈
- 枥
- 殁
- 鲢
- 腭
- 弭
- 镕
- 篑
- 馕
- 堃
- 锑
- 搧
- 闾
- 囫
- 囵
- 鞑
- 辊
- 魟
- 𫚉
- 鲼
- 郅
- 坭
- 栌
- 佗
- 驮
- 哕
- 颦
- 偌
- 颀
- 耜
- 仞
- 贲
- 烀
- 瘢
- 祚
- 悭
- 沢
- 瑠
- 钼
- 鹧
- 鸪
- 蛳
- 苞
- 柃
- 麂
- 暌
- 刎
- 溟
- 菘
- 钐
- 蹉
- 跎
- 篁
- 耆
- 纡
- 熵
- 簪
- 铋
- 幔
- 巳
- 陉
- 増
- 鹁
- 矬
- 锉
- 偈
- 篼
- 龃
- 龉
- 郇
- 孑
- 忒
- 龌
- 稞
- 囔
- 蝮
- 蠊
- 苫
- 菅
- 霪
- 藁
- 膈
- 敕
- 潸
- 槃
- 湎
- 椟
- 茼
- 戗
- 奁
- 芗
- 褔
- 稹
- 澧
- 嬴
- 铍
- 潆
- 橐
- 堺
- 佚
- 嫒
- 葳
- 氚
- 酚
- 椤
- 赉
- 砭
- 匏
- 戾
- 恁
- 腴
- 蛉
- 麸
- 玑
- 痍
- 啜
- 劾
- 忖
- 蛔
- 芾
- 餍
- 诤
- 逋
- 鸵
- 荸
- 夔
- 懑
- 嘏
- 檗
- 牠
- 痔
- 酞
- 猹
- 盅
- 旖
- 鸫
- 椴
- 戍
- 耪
- 豇
- 牍
- 铑
- 噻
- 龅
- 猁
- 蝽
- 欸
- 肱
- 桴
- 镏
- 缬
- 怫
- 唑
- 曈
- 缛
- 吠
- 歙
- 谖
- 俟
- 刽
- 槭
- 硖
- 髯
- 饯
- 藐
- 娈
- 勐
- 颧
- 荻
- 焗
- 鳃
- 昴
- 黟
- 羧
- 趵
- 澶
- 骞
- 鸩
- 婢
- 圄
- 佝
- 偻
- 嗫
- 囯
- 跬
- 朕
- 袅
- 锲
- 杵
- 豢
- 骺
- 诹
- 椹
- 谮
- 㶧
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
macaron_style: true
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: '202205'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| 871e20ffe1396da50bb776ef372c26e6 |
jhakaran1/bert-trainer | jhakaran1 | bert | 8 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,314 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1889
- Accuracy: 0.6437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.751 | 1.0 | 3677 | 0.7828 | 0.6592 |
| 0.6364 | 2.0 | 7354 | 0.8904 | 0.6374 |
| 0.4125 | 3.0 | 11031 | 1.1889 | 0.6437 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
| fb506adb7aae73b68ab1af4c761cfecd |
htermotto/distilbert-base-uncased-finetuned-sngp-squad-seed-42 | htermotto | distilbert | 20 | 2 | transformers | 0 | null | true | false | false | apache-2.0 | null | ['squad_v2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,248 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sngp-squad-seed-42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9074
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4521 | 1.0 | 8248 | 2.0439 |
| 2.1298 | 2.0 | 16496 | 1.9074 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
| c04c287a676728d855325fcd3667ee06 |
tensorspeech/tts-fastspeech2-kss-ko | tensorspeech | null | 5 | 0 | tensorflowtts | 1 | text-to-speech | false | false | false | apache-2.0 | ['ko'] | ['KSS'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | true | true | 2,047 | false |
# FastSpeech2 trained on KSS (Korean)
This repository provides a pretrained [FastSpeech2](https://arxiv.org/abs/2006.04558) trained on KSS dataset (Ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
```
pip install TensorFlowTTS
```
### Converting your Text to Mel Spectrogram
```python
import numpy as np
import soundfile as sf
import yaml
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech2-kss-ko")
fastspeech2 = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech2-kss-ko")
text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
input_ids = processor.text_to_sequence(text)
mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
)
```
#### Referencing FastSpeech2
```
@misc{ren2021fastspeech,
title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
year={2021},
eprint={2006.04558},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
#### Referencing TensorFlowTTS
```
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}
``` | a447cd51885d7c6dcf1933a7f2bee9b2 |
muhtasham/small-mlm-glue-sst2-target-glue-mrpc | muhtasham | bert | 10 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,935 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-mlm-glue-sst2-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7182
- Accuracy: 0.7917
- F1: 0.8571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3886 | 4.35 | 500 | 0.6884 | 0.7892 | 0.8617 |
| 0.0692 | 8.7 | 1000 | 1.3709 | 0.7917 | 0.8627 |
| 0.0318 | 13.04 | 1500 | 1.4689 | 0.7892 | 0.8562 |
| 0.0266 | 17.39 | 2000 | 1.8846 | 0.7745 | 0.8544 |
| 0.0102 | 21.74 | 2500 | 1.7656 | 0.7941 | 0.8571 |
| 0.0139 | 26.09 | 3000 | 1.7271 | 0.7892 | 0.8552 |
| 0.0168 | 30.43 | 3500 | 1.7505 | 0.7966 | 0.8600 |
| 0.0152 | 34.78 | 4000 | 1.6538 | 0.7843 | 0.8483 |
| 0.0135 | 39.13 | 4500 | 1.7268 | 0.7941 | 0.8618 |
| 0.0148 | 43.48 | 5000 | 1.7182 | 0.7917 | 0.8571 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| d18aec38e1f175e943634bc2a831035d |
mlstudent/finetuning-sentiment-model-3000-samples | mlstudent | distilbert | 20 | 11 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,054 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6646
- Accuracy: 0.632
- F1: 0.4321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
| b2953d020620680a5fef6043140e67ee |
venetis/hf_train_output | venetis | vit | 9 | 0 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['rock-glacier-dataset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 6,859 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hf_train_output
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rock-glacier-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3894
- Accuracy: 0.9258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5619 | 0.55 | 50 | 0.5432 | 0.7692 |
| 0.4582 | 1.1 | 100 | 0.4435 | 0.8352 |
| 0.3548 | 1.65 | 150 | 0.3739 | 0.8599 |
| 0.217 | 2.2 | 200 | 0.2913 | 0.9093 |
| 0.1709 | 2.75 | 250 | 0.2619 | 0.9148 |
| 0.0919 | 3.3 | 300 | 0.2475 | 0.9148 |
| 0.0652 | 3.85 | 350 | 0.3275 | 0.8901 |
| 0.0495 | 4.4 | 400 | 0.2515 | 0.9093 |
| 0.0321 | 4.95 | 450 | 0.2878 | 0.9066 |
| 0.0247 | 5.49 | 500 | 0.2612 | 0.9148 |
| 0.017 | 6.04 | 550 | 0.2687 | 0.9176 |
| 0.0131 | 6.59 | 600 | 0.3062 | 0.9093 |
| 0.0113 | 7.14 | 650 | 0.2587 | 0.9231 |
| 0.0099 | 7.69 | 700 | 0.2815 | 0.9203 |
| 0.009 | 8.24 | 750 | 0.2675 | 0.9286 |
| 0.0084 | 8.79 | 800 | 0.2711 | 0.9286 |
| 0.0077 | 9.34 | 850 | 0.2663 | 0.9313 |
| 0.0073 | 9.89 | 900 | 0.3003 | 0.9258 |
| 0.0069 | 10.44 | 950 | 0.2758 | 0.9313 |
| 0.0064 | 10.99 | 1000 | 0.2999 | 0.9258 |
| 0.0061 | 11.54 | 1050 | 0.2931 | 0.9313 |
| 0.0057 | 12.09 | 1100 | 0.2989 | 0.9313 |
| 0.0056 | 12.64 | 1150 | 0.2974 | 0.9313 |
| 0.0053 | 13.19 | 1200 | 0.3099 | 0.9258 |
| 0.005 | 13.74 | 1250 | 0.3131 | 0.9313 |
| 0.0049 | 14.29 | 1300 | 0.3201 | 0.9258 |
| 0.0046 | 14.84 | 1350 | 0.3109 | 0.9313 |
| 0.0045 | 15.38 | 1400 | 0.3168 | 0.9313 |
| 0.0043 | 15.93 | 1450 | 0.3226 | 0.9231 |
| 0.0042 | 16.48 | 1500 | 0.3234 | 0.9231 |
| 0.0041 | 17.03 | 1550 | 0.3283 | 0.9258 |
| 0.0039 | 17.58 | 1600 | 0.3304 | 0.9258 |
| 0.0038 | 18.13 | 1650 | 0.3321 | 0.9231 |
| 0.0037 | 18.68 | 1700 | 0.3362 | 0.9231 |
| 0.0036 | 19.23 | 1750 | 0.3307 | 0.9286 |
| 0.0035 | 19.78 | 1800 | 0.3357 | 0.9231 |
| 0.0034 | 20.33 | 1850 | 0.3244 | 0.9313 |
| 0.0033 | 20.88 | 1900 | 0.3497 | 0.9231 |
| 0.0032 | 21.43 | 1950 | 0.3443 | 0.9231 |
| 0.0031 | 21.98 | 2000 | 0.3398 | 0.9286 |
| 0.003 | 22.53 | 2050 | 0.3388 | 0.9286 |
| 0.003 | 23.08 | 2100 | 0.3399 | 0.9286 |
| 0.0029 | 23.63 | 2150 | 0.3548 | 0.9231 |
| 0.0028 | 24.18 | 2200 | 0.3475 | 0.9286 |
| 0.0028 | 24.73 | 2250 | 0.3480 | 0.9286 |
| 0.0027 | 25.27 | 2300 | 0.3542 | 0.9231 |
| 0.0026 | 25.82 | 2350 | 0.3589 | 0.9231 |
| 0.0026 | 26.37 | 2400 | 0.3449 | 0.9286 |
| 0.0025 | 26.92 | 2450 | 0.3604 | 0.9231 |
| 0.0025 | 27.47 | 2500 | 0.3493 | 0.9286 |
| 0.0024 | 28.02 | 2550 | 0.3631 | 0.9258 |
| 0.0024 | 28.57 | 2600 | 0.3590 | 0.9258 |
| 0.0023 | 29.12 | 2650 | 0.3604 | 0.9258 |
| 0.0023 | 29.67 | 2700 | 0.3667 | 0.9258 |
| 0.0022 | 30.22 | 2750 | 0.3571 | 0.9286 |
| 0.0022 | 30.77 | 2800 | 0.3660 | 0.9258 |
| 0.0021 | 31.32 | 2850 | 0.3638 | 0.9286 |
| 0.0021 | 31.87 | 2900 | 0.3729 | 0.9258 |
| 0.0021 | 32.42 | 2950 | 0.3706 | 0.9258 |
| 0.002 | 32.97 | 3000 | 0.3669 | 0.9286 |
| 0.002 | 33.52 | 3050 | 0.3740 | 0.9258 |
| 0.002 | 34.07 | 3100 | 0.3693 | 0.9286 |
| 0.002 | 34.62 | 3150 | 0.3700 | 0.9286 |
| 0.0019 | 35.16 | 3200 | 0.3752 | 0.9258 |
| 0.0019 | 35.71 | 3250 | 0.3753 | 0.9258 |
| 0.0019 | 36.26 | 3300 | 0.3721 | 0.9286 |
| 0.0018 | 36.81 | 3350 | 0.3764 | 0.9258 |
| 0.0018 | 37.36 | 3400 | 0.3758 | 0.9258 |
| 0.0018 | 37.91 | 3450 | 0.3775 | 0.9258 |
| 0.0018 | 38.46 | 3500 | 0.3812 | 0.9258 |
| 0.0018 | 39.01 | 3550 | 0.3817 | 0.9258 |
| 0.0017 | 39.56 | 3600 | 0.3815 | 0.9258 |
| 0.0017 | 40.11 | 3650 | 0.3825 | 0.9258 |
| 0.0017 | 40.66 | 3700 | 0.3852 | 0.9258 |
| 0.0017 | 41.21 | 3750 | 0.3854 | 0.9258 |
| 0.0017 | 41.76 | 3800 | 0.3823 | 0.9258 |
| 0.0016 | 42.31 | 3850 | 0.3829 | 0.9258 |
| 0.0016 | 42.86 | 3900 | 0.3873 | 0.9258 |
| 0.0016 | 43.41 | 3950 | 0.3842 | 0.9258 |
| 0.0016 | 43.96 | 4000 | 0.3857 | 0.9258 |
| 0.0016 | 44.51 | 4050 | 0.3873 | 0.9258 |
| 0.0016 | 45.05 | 4100 | 0.3878 | 0.9258 |
| 0.0016 | 45.6 | 4150 | 0.3881 | 0.9258 |
| 0.0016 | 46.15 | 4200 | 0.3888 | 0.9258 |
| 0.0016 | 46.7 | 4250 | 0.3891 | 0.9258 |
| 0.0016 | 47.25 | 4300 | 0.3878 | 0.9258 |
| 0.0016 | 47.8 | 4350 | 0.3890 | 0.9258 |
| 0.0016 | 48.35 | 4400 | 0.3890 | 0.9258 |
| 0.0015 | 48.9 | 4450 | 0.3895 | 0.9258 |
| 0.0015 | 49.45 | 4500 | 0.3896 | 0.9258 |
| 0.0015 | 50.0 | 4550 | 0.3894 | 0.9258 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
| 15175f3f6e5ebe45fe024d8ff40fa8a8 |
sd-concepts-library/ohisashiburi-style | sd-concepts-library | null | 9 | 0 | null | 1 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,113 | false | ### ohisashiburi-style on Stable Diffusion
This is the `<ohishashiburi-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
| a1b1c531c2be6b824684bd2a6ac09ec1 |
tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa | tiennvcs | layoutlmv2 | 15 | 525 | transformers | 3 | document-question-answering | true | false | false | cc-by-sa-4.0 | null | null | null | 0 | 0 | 0 | 0 | 1 | 0 | 1 | ['generated_from_trainer'] | true | true | true | 1,507 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-base-uncased-finetuned-docvqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.463 | 0.27 | 1000 | 1.6272 |
| 0.9447 | 0.53 | 2000 | 1.3646 |
| 0.7725 | 0.8 | 3000 | 1.2560 |
| 0.5762 | 1.06 | 4000 | 1.3582 |
| 0.4382 | 1.33 | 5000 | 1.2490 |
| 0.4515 | 1.59 | 6000 | 1.1860 |
| 0.383 | 1.86 | 7000 | 1.1940 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3
| e45abe4f410ae5756d1dff717fd54b7a |
Dagar/t5-small-science-papers | Dagar | t5 | 11 | 5 | transformers | 1 | text2text-generation | true | false | false | apache-2.0 | null | ['scientific_papers'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,840 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-science-papers
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6405
- Rouge1: 12.3568
- Rouge2: 2.4449
- Rougel: 10.2371
- Rougelsum: 11.4209
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 4.4735 | 1.0 | 12690 | 4.3727 | 9.9604 | 1.7641 | 8.6213 | 9.2779 | 19.0 |
| 4.0104 | 2.0 | 25380 | 3.9384 | 11.4001 | 2.1474 | 9.6516 | 10.6602 | 19.0 |
| 3.8237 | 3.0 | 38070 | 3.7580 | 11.1806 | 2.1229 | 9.3881 | 10.3853 | 19.0 |
| 3.7382 | 4.0 | 50760 | 3.6738 | 11.9298 | 2.3222 | 9.9077 | 11.045 | 19.0 |
| 3.6994 | 5.0 | 63450 | 3.6405 | 12.3568 | 2.4449 | 10.2371 | 11.4209 | 19.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
| f1d455d6892fd2b4cd8457efcb2fce8b |
deepset/gbert-base | deepset | null | 8 | 38,318 | transformers | 21 | fill-mask | true | true | false | mit | ['de'] | ['wikipedia', 'OPUS', 'OpenLegalData'] | null | 0 | 0 | 0 | 0 | 2 | 2 | 0 | [] | false | true | true | 1,804 | false |
# German BERT base
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model and show that it outperforms its predecessors.
## Overview
**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)
**Architecture:** BERT base
**Language:** German
## Performance
```
GermEval18 Coarse: 78.17
GermEval18 Fine: 50.90
GermEval14: 87.98
```
See also:
deepset/gbert-base
deepset/gbert-large
deepset/gelectra-base
deepset/gelectra-large
deepset/gelectra-base-generator
deepset/gelectra-large-generator
## Authors
Branden Chan: `branden.chan [at] deepset.ai`
Stefan Schweter: `stefan [at] schweter.eu`
Timo Möller: `timo.moeller [at] deepset.ai`
## About us
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
- [FARM](https://github.com/deepset-ai/FARM)
- [Haystack](https://github.com/deepset-ai/haystack/)
Get in touch:
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs)
| f37d2e27e24d21cf078eed3e3f9478f1 |
jdeboever/xlm-roberta-base-finetuned-panx-de | jdeboever | xlm-roberta | 14 | 25 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,314 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| ce31ce6298f038c5e8e68376d9261bbc |
muhtasham/tiny-mlm-glue-qnli-target-glue-stsb | muhtasham | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,738 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-qnli-target-glue-stsb
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8934
- Pearson: 0.8154
- Spearmanr: 0.8157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 2.952 | 2.78 | 500 | 1.1581 | 0.7199 | 0.7571 |
| 0.9583 | 5.56 | 1000 | 1.1118 | 0.7743 | 0.7995 |
| 0.7459 | 8.33 | 1500 | 0.9843 | 0.8028 | 0.8182 |
| 0.6197 | 11.11 | 2000 | 0.8616 | 0.8165 | 0.8217 |
| 0.5182 | 13.89 | 2500 | 0.9113 | 0.8140 | 0.8169 |
| 0.4676 | 16.67 | 3000 | 0.9804 | 0.8144 | 0.8183 |
| 0.4128 | 19.44 | 3500 | 0.8934 | 0.8154 | 0.8157 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 62a2db2ddda8e9c2ec4867d7712ed32f |
KETI-AIR/ke-t5-large | KETI-AIR | t5 | 9 | 2,078 | transformers | 3 | text2text-generation | true | true | true | apache-2.0 | ['en', 'ko'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['t5'] | false | true | true | 2,373 | false |
# ke-t5 base
Pretrained T5 Model on Korean and English. See [Github](https://github.com/AIRC-KETI/ke-t5) and [Paper](https://aclanthology.org/2021.findings-emnlp.33/) [Korean paper](https://koreascience.kr/article/CFKO202130060717834.pdf) for more details.
## How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("KETI-AIR/ke-t5-large")
tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-large")
```
## BibTeX entry and citation info
```bibtex
@inproceedings{kim-etal-2021-model-cross,
title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems",
author = "Kim, San and
Jang, Jin Yea and
Jung, Minyoung and
Shin, Saim",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.33",
doi = "10.18653/v1/2021.findings-emnlp.33",
pages = "352--365",
abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.",
}
``` | 0e8ba1f61686636d6df6b5864acc2d64 |
alla1101/distilbert-base-uncased-finetuned-emotion | alla1101 | distilbert | 12 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,343 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2236
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3293 | 0.901 | 0.8979 |
| No log | 2.0 | 500 | 0.2236 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| a355f14051f873607cc161d552a5caf7 |