---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
  results: []
---

<!-- 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-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8433
- Wer: 0.9852

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.468         | 166.67 | 500  | 3.0262          | 1.0035 |
| 0.0572        | 333.33 | 1000 | 3.5352          | 0.9721 |
| 0.0209        | 500.0  | 1500 | 3.7266          | 0.9834 |
| 0.0092        | 666.67 | 2000 | 3.8433          | 0.9852 |

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0


Thanks for checking this out! <br />
This video explains the ideas behind KerasBERT (still very much a work in progress)
https://www.youtube.com/watch?v=J3P8WLAELqk


---
tags:
- conversational
---

#Chizuru Ichinose~ DialoGPT Model


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7034313725490197
    - name: F1
      type: f1
      value: 0.8207407407407408
---

<!-- 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-mrpc

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5708
- Accuracy: 0.7034
- F1: 0.8207

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 58   | 0.5708          | 0.7034   | 0.8207 |

### Framework versions

- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: cola
    metrics:
    - name: Matthews Correlation
      type: matthews_correlation
      value: 0.533214904586951
---

<!-- 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-cola

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5774
- Matthews Correlation: 0.5332

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2347        | 1.0   | 535  | 0.5774          | 0.5332               |

### Framework versions

- Transformers 4.11.0
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task

Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.

LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question is answered.

> Example input: [CLS] Your question [SEP] The context to be searched in [SEP]

Link to the original model: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext

Credits to the paper:

> @misc{pubmedbert,   author = {Yu Gu and Robert Tinn and Hao Cheng and
> Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann
> and Jianfeng Gao and Hoifung Poon},   title = {Domain-Specific
> Language Model Pretraining for Biomedical Natural Language
> Processing},   year = {2020},   eprint = {arXiv:2007.15779}, }


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model_index:
- name: t5-small-finetuned-toxic
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    metric:
      name: Rouge1
      type: rouge
      value: 93.7659
---

<!-- 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-finetuned-toxic

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1295
- Rouge1: 93.7659
- Rouge2: 3.6618
- Rougel: 93.7652
- Rougelsum: 93.7757
- Gen Len: 2.5481

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.1595        | 1.0   | 7979 | 0.1295          | 93.7659 | 3.6618 | 93.7652 | 93.7757   | 2.5481  |

### Framework versions

- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model_index:
- name: t5-small-finetuned-xsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: xsum
      type: xsum
      args: default
    metric:
      name: Rouge1
      type: rouge
      value: 29.6452
---

<!-- 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-finetuned-xsum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3833
- Rouge1: 29.6452
- Rouge2: 8.6953
- Rougel: 23.4474
- Rougelsum: 23.4553
- Gen Len: 18.8037

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6051        | 1.0   | 102023 | 2.3833          | 29.6452 | 8.6953 | 23.4474 | 23.4553   | 18.8037 |

### Framework versions

- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model_index:
- name: t5_large_baseline
  results:
  - task:
      name: Summarization
      type: summarization
    metric:
      name: Rouge1
      type: rouge
      value: 99.8958
---

<!-- 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_large_baseline

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Rouge1: 99.8958
- Rouge2: 99.8696
- Rougel: 99.8958
- Rougelsum: 99.8958
- Gen Len: 46.715

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.9852        | 0.33  | 50   | 0.1098          | 55.1421 | 49.8248 | 54.4294 | 54.7377   | 19.0    |
| 0.1186        | 0.67  | 100  | 0.0176          | 58.0994 | 54.8973 | 57.7383 | 57.9538   | 19.0    |
| 0.0417        | 1.0   | 150  | 0.0057          | 58.3685 | 55.7353 | 58.279  | 58.2729   | 19.0    |
| 0.0225        | 1.33  | 200  | 0.0029          | 58.8981 | 56.2457 | 58.8202 | 58.7906   | 19.0    |
| 0.0131        | 1.67  | 250  | 0.0024          | 58.8439 | 56.2535 | 58.7557 | 58.7218   | 19.0    |
| 0.0112        | 2.0   | 300  | 0.0013          | 58.9538 | 56.4749 | 58.9322 | 58.8817   | 19.0    |
| 0.0077        | 2.33  | 350  | 0.0013          | 58.9538 | 56.4749 | 58.9322 | 58.8817   | 19.0    |
| 0.0043        | 2.67  | 400  | 0.0010          | 59.0124 | 56.5806 | 58.9867 | 58.9342   | 19.0    |
| 0.0052        | 3.0   | 450  | 0.0010          | 59.0402 | 56.6982 | 59.0385 | 58.986    | 19.0    |

### Framework versions

- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.11.0
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- sentiment-analysis
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-finance-sentiment-kaggle
  results: []
---

<!-- 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-finance-sentiment-kaggle

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: 1.2522
- Accuracy: 0.8333

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 455  | 1.1193          | 0.8185   |
| 0.1631        | 2.0   | 910  | 0.9645          | 0.8251   |
| 0.1303        | 3.0   | 1365 | 1.0961          | 0.8119   |
| 0.0455        | 4.0   | 1820 | 1.1955          | 0.8333   |
| 0.0148        | 5.0   | 2275 | 1.2522          | 0.8333   |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es-v1
  results: []
---

<!-- 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-amazon-en-es-v1

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0393
- Rouge1: 17.2431
- Rouge2: 8.087
- Rougel: 16.9132
- Rougelsum: 16.9485

## 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: 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.6665        | 1.0   | 1209 | 3.2917          | 13.8229 | 5.5115 | 13.3845 | 13.4326   |
| 3.8961        | 2.0   | 2418 | 3.1711          | 16.2336 | 8.5449 | 15.6123 | 15.5944   |
| 3.5801        | 3.0   | 3627 | 3.0917          | 17.3138 | 8.1576 | 16.709  | 16.6737   |
| 3.4258        | 4.0   | 4836 | 3.0583          | 16.1301 | 7.7461 | 15.7185 | 15.7575   |
| 3.3154        | 5.0   | 6045 | 3.0573          | 17.5471 | 8.7643 | 17.2062 | 17.1174   |
| 3.2438        | 6.0   | 7254 | 3.0479          | 17.161  | 8.0785 | 16.8794 | 16.8996   |
| 3.2024        | 7.0   | 8463 | 3.0377          | 17.2455 | 8.1524 | 16.8894 | 16.9402   |
| 3.1745        | 8.0   | 9672 | 3.0393          | 17.2431 | 8.087  | 16.9132 | 16.9485   |

### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0


---
tags:
- conversational
---

#harry potter dialogpt model


---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
model-index:
- name: ''
  results: []
---

<!-- 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 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5258
- Wer: 1.0073

## 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: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.917         | 16.13 | 500  | 4.8963          | 1.0    |
| 3.3585        | 32.25 | 1000 | 3.3069          | 1.0000 |
| 1.5873        | 48.38 | 1500 | 0.8274          | 1.0061 |
| 1.2654        | 64.51 | 2000 | 0.6250          | 1.0076 |
| 1.0917        | 80.64 | 2500 | 0.5460          | 1.0056 |
| 1.0001        | 96.76 | 3000 | 0.5304          | 1.0083 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0


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Sin Tiempo Para Morir De La historia original
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Sin Tiempo Para Morir la película 2021


---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: shopee-ner
  results: []
---

<!-- 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. -->

# shopee-ner

This model is a fine-tuned version of [cahya/xlm-roberta-base-indonesian-NER](https://huggingface.co/cahya/xlm-roberta-base-indonesian-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2046
- Precision: 0.7666
- Recall: 0.8666
- F1: 0.8135
- Accuracy: 0.9320

## 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2282        | 1.0   | 33750 | 0.2174          | 0.7443    | 0.8506 | 0.7939 | 0.9253   |
| 0.1983        | 2.0   | 67500 | 0.2046          | 0.7666    | 0.8666 | 0.8135 | 0.9320   |

### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3


---
tags:
- conversational
---

# Harry Potter DialoGPT model


---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt2-finetuned-reddit-aita-text-gen
  results: []
---

<!-- 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. -->

# distilgpt2-finetuned-reddit-aita-text-gen

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
- training_precision: float32

### Training results

### Framework versions

- Transformers 4.14.1
- TensorFlow 2.7.0
- Datasets 1.16.1
- Tokenizers 0.10.3


---
language:
  - pt
---

Image Captioning in Portuguese trained with ViT and GPT2

[DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese)

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)


---
language:
  - pt
---

This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)

## Usage

```python
from transformers import AutoTokenizer  # Or BertTokenizer
from transformers import AutoModelForPreTraining  # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel  # or BertModel, for BERT without pretraining heads
model = AutoModelForPreTraining.from_pretrained('adalbertojunior/distilbert-portuguese-cased')
tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False)
```
You should fine tune it on your own data.

It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks.


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: cola
    metrics:
    - name: Matthews Correlation
      type: matthews_correlation
      value: 0.509687043672971
---

<!-- 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-cola

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7512
- Matthews Correlation: 0.5097

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5237        | 1.0   | 535  | 0.5117          | 0.4469               |
| 0.3496        | 2.0   | 1070 | 0.5538          | 0.4965               |
| 0.2377        | 3.0   | 1605 | 0.6350          | 0.4963               |
| 0.1767        | 4.0   | 2140 | 0.7512          | 0.5097               |
| 0.1383        | 5.0   | 2675 | 0.8647          | 0.5056               |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.8.1+cu102
- Datasets 1.15.1
- Tokenizers 0.10.1


---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "Stocks rallied and the British pound gained."
---

FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium.

The model will give softmax outputs for three labels: positive, negative or neutral.

---

About Prosus

Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com.

Contact information

Please contact Dogu Araci dogu.araci[at]prosus[dot]com and Zulkuf Genc zulkuf.genc[at]prosus[dot]com about any FinBERT related issues and questions.


---
tags:
- conversational
---

#Rick Sanchez DiaploGPT Model


---
tags:
- conversational
---

# Celestia Ludenburg DiabloGPT Model


---
tags:
- conversational
---

# The Vampire Diaries DialoGPT Model


---
tags:
- Conversational
---
# Tony Stark DialoGPT Model


This model is a demonstration of the [Ernie library] (https://github.com/labteral/ernie) for fine-tuning sentence classification models. The base model is bert-base-uncased. The dataset for fine-tuning consists of two labeled example sentences. Because this is a toy example, we do not recommend it for anything other than for demonstrating the Ernie integration with Huggingface Hub.


## AllenAI's <i>scibert_scivocab_uncased</i> fine-tuned on SQuAD 2.0 evaluated with F1 = 86.85

#### To load the model:

```
from transformers import BertTokenizerFast
from transformers import BertForQuestionAnswering

tokenizer = BertTokenizerFast.from_pretrained("LoudlySoft/scibert_scivocab_uncased_squad")
model = BertForQuestionAnswering.from_pretrained("LoudlySoft/scibert_scivocab_uncased_squad")
```


# LIMIT-BERT

Code and model for the *EMNLP 2020 Findings* paper:

[LIMIT-BERT: Linguistic Informed Multi-task BERT](https://arxiv.org/abs/1910.14296))

## Contents

1. [Requirements](#Requirements)
2. [Training](#Training)

## Requirements

* Python 3.6 or higher.
* Cython 0.25.2 or any compatible version.
* [PyTorch](http://pytorch.org/) 1.0.0+.
* [EVALB](http://nlp.cs.nyu.edu/evalb/). Before starting, run `make` inside the `EVALB/` directory to compile an `evalb` executable. This will be called from Python for evaluation.
* [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) PyTorch 1.0.0+ or any compatible version.

#### Pre-trained Models (PyTorch)
The following pre-trained models are available for download from Google Drive:
* [`LIMIT-BERT`](https://drive.google.com/open?id=1fm0cK2A91iLG3lCpwowCCQSALnWS2X4i):
  PyTorch version, same setting with BERT-Large-WWM，loading model with [pytorch-transformers](https://github.com/huggingface/pytorch-transformers).

## How to use

```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cooelf/limitbert")
model = AutoModel.from_pretrained("cooelf/limitbert")
```

Please see our original repo for the training scripts.

https://github.com/cooelf/LIMIT-BERT

## Training

To train LIMIT-BERT, simply run:
```
sh run_limitbert.sh
```
### Evaluation Instructions

To test after setting model path:
```
sh test_bert.sh
```

## Citation

```
@article{zhou2019limit,
  title={{LIMIT-BERT}: Linguistic informed multi-task {BERT}},
  author={Zhou, Junru and Zhang, Zhuosheng and Zhao, Hai},
  journal={arXiv preprint arXiv:1910.14296},
  year={2019}
}
```


Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large


---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
  results: []
---

<!-- 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 was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 11.9582
- Wer: 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 6.4818        | 0.28  | 500  | 10.9410         | 1.0 |
| 6.7531        | 0.56  | 1000 | 9.4289          | 1.0 |
| 6.8635        | 0.84  | 1500 | 9.7160          | 1.0 |
| 6.8625        | 1.12  | 2000 | 9.3706          | 1.0 |
| 7.0072        | 1.4   | 2500 | 9.2437          | 1.0 |
| 6.8906        | 1.68  | 3000 | 10.7605         | 1.0 |
| 6.9136        | 1.96  | 3500 | 11.3213         | 1.0 |
| 6.8846        | 2.24  | 4000 | 10.8779         | 1.0 |
| 6.9842        | 2.52  | 4500 | 11.9320         | 1.0 |
| 6.8026        | 2.8   | 5000 | 11.9582         | 1.0 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0


---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
  results: []
---

<!-- 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 was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 2.0097

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9015        | 0.28  | 500  | 7.0241          | 1.0    |
| 6.7728        | 0.56  | 1000 | 6.9759          | 1.0    |
| 6.5915        | 0.84  | 1500 | 6.9615          | 2.0097 |
| 0.0           | 1.12  | 2000 | nan             | 2.0097 |
| 0.0           | 1.4   | 2500 | nan             | 2.0097 |
| 0.0           | 1.68  | 3000 | nan             | 2.0097 |
| 0.0           | 1.96  | 3500 | nan             | 2.0097 |
| 0.0           | 2.24  | 4000 | nan             | 2.0097 |
| 0.0           | 2.52  | 4500 | nan             | 2.0097 |
| 0.0           | 2.8   | 5000 | nan             | 2.0097 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-russian-big-kaggle
  results: []
---

<!-- 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-russian-big-kaggle

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: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
  name: wav2vec2-base-russian-modified-kaggle
---

<!-- 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-russian-modified-kaggle

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3


---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-russian-demo-kaggle
  results: []
---

<!-- 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-russian-demo-kaggle

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.9997

## 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: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0102        | 1.03  | 500   | inf             | 0.9997 |
| 0.0068        | 2.06  | 1000  | inf             | 0.9997 |
| 0.0           | 3.09  | 1500  | inf             | 0.9997 |
| 0.0313        | 4.12  | 2000  | inf             | 0.9997 |
| 0.0           | 5.15  | 2500  | inf             | 0.9997 |
| 0.0052        | 6.19  | 3000  | inf             | 0.9997 |
| 0.0287        | 7.22  | 3500  | inf             | 0.9997 |
| 0.0           | 8.25  | 4000  | inf             | 0.9997 |
| 0.01          | 9.28  | 4500  | inf             | 0.9997 |
| 0.0           | 10.31 | 5000  | inf             | 0.9997 |
| 0.3919        | 11.34 | 5500  | inf             | 0.9997 |
| 0.0           | 12.37 | 6000  | inf             | 0.9997 |
| 0.0           | 13.4  | 6500  | inf             | 0.9997 |
| 0.0           | 14.43 | 7000  | inf             | 0.9997 |
| 0.6422        | 15.46 | 7500  | inf             | 0.9997 |
| 0.0           | 16.49 | 8000  | inf             | 0.9997 |
| 0.0           | 17.53 | 8500  | inf             | 0.9997 |
| 0.0           | 18.56 | 9000  | inf             | 0.9997 |
| 0.0           | 19.59 | 9500  | inf             | 0.9997 |
| 0.0           | 20.62 | 10000 | inf             | 0.9997 |
| 0.0427        | 21.65 | 10500 | inf             | 0.9997 |
| 0.0           | 22.68 | 11000 | inf             | 0.9997 |
| 0.0           | 23.71 | 11500 | inf             | 0.9997 |
| 0.0           | 24.74 | 12000 | inf             | 0.9997 |
| 0.0091        | 25.77 | 12500 | inf             | 0.9997 |
| 0.1243        | 26.8  | 13000 | inf             | 0.9997 |
| 0.0           | 27.83 | 13500 | inf             | 0.9997 |
| 0.0           | 28.87 | 14000 | inf             | 0.9997 |
| 0.0           | 29.9  | 14500 | inf             | 0.9997 |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3


---
language: ta
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Rajaram1996/wav2vec2-large-xlsr-tamil
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice ta
      type: common_voice
      args: ta
    metrics:
       - name: Test WER
         type: wer
         value: 69.76
---
# Wav2Vec2-Large-XLSR-53-tamil

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage
The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "ta", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("Rajaram1996/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Rajaram1996/wav2vec2-large-xlsr-53-tamil")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```

## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.

```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "ta", split="test")

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("Rajaram1996/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Rajaram1996/wav2vec2-large-xlsr-53-tamil")

model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 69.76 %


---
inference: true
pipeline_tag: audio-classification
tags:
- speech
- audio
- HUBert
---
A place to hold the model for easier inference.


---
tags:
- image-classification
- pytorch

inference: true
pipeline_tag: image-classification

metrics:
- accuracy

model-index:
- name: FacialEmoRecog
  results:
  - task:
      name: Image Classification
      type: image-classification
  - metrics:
      name: Accuracy
      type: accuracy
      value: 0.9189583659172058
---

# FacialEmoRecog
Create your own image classifier for **anything** by running this repo

 ## Example Images


---
tags:
- conversational
---
# My Awesome Model


---
tags:
- conversational
---

# My Awesome Model


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
  results: []
---

<!-- 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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2414

## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3036        | 1.0   | 4427 | 1.2414          |

### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3


---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy

model-index:
- name: vision-transformer-fmri-classification-ft
  results:
  - task:
      name: Image Classification
      type: image-classification
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7955589294433594
---

# vision-transformer-fmri-classification-ft

Autogenerated by HuggingPics🤗🖼

Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).

Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).

## Example Images


---
tags:
- conversational
---

# Bobby Hill DialoGPT Model


---
tags:
- summarization
language:
- dutch
datasets:
- cnn_dm_nl
widget:
- text: "(CNN) Skywatchers in West-Noord-Amerika zijn in voor een traktatie: een bijna vijf minuten totale maansverduistering vanmorgen. Hier is hoe het zich ontvouwt:. Het begon om 3:16 a.m. Pacific Daylight Tijd, toen de maan begon te bewegen in de schaduw van de Aarde. Voor het volgende uur en 45 minuten, die schaduw zal bewegen over de maan en verzwolgen het om 4:58 a.m. Pacific Time. De totale verduistering zal slechts vier minuten en 43 seconden duren, en NASA zegt dat maakt het de kortste van de eeuw. Kijken live op NASA TV. Terwijl mensen ten westen van de Mississippi River zal het beste uitzicht hebben, ten minste een gedeeltelijke verduistering zal zichtbaar zijn over de hele natie. Maar zonsopgang zal de show te onderbreken op de Oostkust. Delen van Zuid-Amerika, India, China en China Een maansverduistering gebeurt wanneer de zon, de aarde en de maan een rechte lijn vormen in de ruimte, met de aarde in het midden. De zon schijnt op de Aarde en creëert een schaduw. Als de maan dieper in die schaduw beweegt, lijkt het donker te worden en lijkt zelfs een roodachtige kleur te zijn. Waarom rood? Omdat de atmosfeer van de Aarde het grootste deel van het blauwe licht filtert. Sommige mensen hebben het effect van de \"bloedmaan\" bijgenaamd. NASA zegt dat maansverduisteringen meestal ten minste twee keer per jaar plaatsvinden, maar deze verduistering is de derde in een reeks van vier op een rij, bekend als een \"tetrad.\" De eerste was op 15 april 2014. De tweede was in september 2014, de volgende is zaterdag en er zal er een meer zijn, op 28 september. Als je meer wilt weten over de verduistering, NASA astronoom Mitzi Adam. Deel uw foto's met CNN iReport."
- text: "(CNN) Filipino's worden gewaarschuwd om op wacht te staan voor flash overstromingen en aardverschuivingen als tropische storm Maysak benaderde de Aziatische eiland natie zaterdag. Slechts een paar dagen geleden, Maysak kreeg super tyfoon status dankzij zijn aanhoudende 150 km/h winden. Het heeft sindsdien verloren veel stoom als het naar het westen in de Stille Oceaan heeft gedraaid. Het is nu geclassificeerd als een tropische storm, volgens de Filipijnse nationale weerdienst, die noemt het een andere naam, Chedeng. Het heeft stabiele winden van meer dan 70 km/h (115 km/h) en gusts tot 90 km/h vanaf 17.00 uur (5 uur ET) Zaterdag. Toch, dat betekent niet dat Maysak zal geen pak een wallop. Autoriteiten nam preventieve stappen om mensen veilig te houden zoals barring outdoor activiteiten zoals zwemmen, surfen, di. Gabriel Llave, een ramp ambtenaar, vertelde PNA dat toeristen die aankomen zaterdag in en rond de kustplaats van Aurora \"zal niet worden geaccepteerd door de eigenaren van hotels, resorts, herbergen en dergelijke... en zal worden geadviseerd om terug te keren naar hun respectievelijke plaatsen.\" Aldczar Aurelio, een meteoroloog met de Filippijnse Atmosferische, Geofysische en Astronomische Diensten Administratie (PAGASA), zei dat de storm was gecentreerd 200 mijl ten zuidwesten van de provincie Aurora vanaf 5 uur (5 uur ET) en richting het westen op een 12.5 mph clip. Het is verwacht dat landval zondagochtend maken op de zuidoostelijke kust van de provincie Isabela en zijn uit de Filippijnen tegen maandag. Ahead van de storm. Isabela Gov. Faustino Dry III waarschuwde zaterdag dat bewoners moet handelen als deze zal maken landfall zondagochtend op de zuidoostelijke kust van de provincie Isabela en zijn uit de Filippijnen voor maandag."

---

# mt5-base-cnn-nl

mt5-base finetuned on CNN DM translated to nl (Dutch).

* Learning rate 1e-3
* Trained for 1 epoch
* Max source length 1024
* Max target length 142

* rouge1  31.1766
* rouge2  8.4538
* rougeL  17.8674


---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt-neo-1.3B
- gpt-neo
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT Neo 1.3B pre-trained on cleaned Dutch mC4  🇳🇱

A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.

## How To Use

You can use this GPT-Neo model directly with a pipeline for text generation.

```python
MODEL_DIR='yhavinga/gpt-neo-1.3B-dutch'
from transformers import pipeline, GPT2Tokenizer, GPTNeoForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPTNeoForCausalLM.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)

generated_text = generator('1 - geel. 2 - groen. 3 -', max_length=60, num_beams=4, no_repeat_ngram_size=3, repetition_penalty=2.0)
```

*"1 - geel. 2 - groen. 3 - rood. 4 - blauw. 5 - bruin. 6 - zwart. 7 - oranje. 8 - roze. 9 - paars. 10 - wit. 11 - grijs. 12 - magenta. 13 - lila. 14 - lichtgroen. 15"*

## Tokenizer

* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

This model was trained on the `full` configuration (33B tokens) of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.

* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.

|                                                                                | model   | params | train seq len | ppl  | loss | batch size | epochs | steps           | optim     | lr     | duration | config    |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M   | 512           | 20.9 | 3.04 | 128        | 1      | 190000/558608          | adafactor | 2.4e-3 | 1d 12h   | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch)   | gpt2    | 345M   | 512           | 15.1 | 2.71 | 128        | 1      | 320000/520502   | adafactor | 8e-4   | 7d 2h    | full      |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch)     | gpt2    | 762M   | 512           | 15.1 | 2.72 | 32         | 1      | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h   | large     |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B   | 512           | 16.0 | 2.77 | 16         | 1      | 960000/3049896  | adafactor | 5e-4   | 7d 11h   | full      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-large
- gpt2
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT2-Large pre-trained on cleaned Dutch mC4 🇳🇱

A GPT2 large model (762M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.

## How To Use

You can use this GPT2-model directly with a pipeline for text generation.

```python
MODEL_DIR='yhavinga/gpt2-large-dutch'
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPT2LMHeadModel.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)

generated_text = generator('Het eiland West-', max_length=100, do_sample=True, top_k=40, top_p=0.95, repetition_penalty=2.0))
```

*"Het eiland West-" - "Terschelling wordt sinds jaar en dag bewoond door de mens. De mensen die in het huidige Terherne wonen doen er alles aan om hun dorp te behouden voor deze diersoort, namelijk; een natuurreservaat dat vooral bestaat uit hoge duinen met lage begroeing waar planten van vroeger worden afgewisseld (zoals wilde hyacinten)en waarop grassen groeien waarvan sommige soorten zeldzame vormen hebben ontwikkeld: duinlelie of blauwe bosbes zijn bijvoorbeeld bekend vanwege onder andere kleurmole"*

## Tokenizer

* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

This model was trained on of the `full` configuration (33B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.

* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.

|                                                                                | model   | params | train seq len | ppl  | loss | batch size | epochs | steps           | optim     | lr     | duration | config    |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M   | 512           | 20.9 | 3.04 | 128        | 1      | 190000/558608          | adafactor | 2.4e-3 | 1d 12h   | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch)   | gpt2    | 345M   | 512           | 15.1 | 2.71 | 128        | 1      | 320000/520502   | adafactor | 8e-4   | 7d 2h    | full      |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch)     | gpt2    | 762M   | 512           | 15.1 | 2.72 | 32         | 1      | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h   | large     |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B   | 512           | 16.0 | 2.77 | 16         | 1      | 960000/3049896  | adafactor | 5e-4   | 7d 11h   | full      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
language:
- nl
datasets:
- yhavinga/mc4_nl_cleaned
tags:
- seq2seq
- lm-head
license: apache-2.0
inference: false
---

# T5-large pre-trained on cleaned Dutch mC4 🇳🇱

A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) v1.1 large model pre-trained from scratch on [Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).

* Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off.
* For a fine-tuned version for summarization, see [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test).
* For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
* T5 paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)

![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)

## Tokenizer

* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

All models listed below are trained on of the `full` configuration (39B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.

* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
  Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
* The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
  with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
  base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
* The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
  Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
  be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
  The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
  base model is probably the better choice.

|                                                                                | model   | train seq len | acc      | loss     | batch size | epochs | steps   | dropout | optim     | lr   | duration |
|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
| [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch)                           | T5      | 512           | 0,70     | 1,38     | 128        | 1      | 528481  | 0.1     | adafactor | 5e-3 | 2d 9h    |
| [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024          | 0,73     | 1,20     | 64         | 2      | 1014525 | 0.0     | adafactor | 5e-3 | 5d 5h    |
| [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased)     | t5-v1.1 | 1024          | **0,78** | **0,96** | 64         | 2      | 1210000 | 0.0     | adafactor | 5e-3 | 6d 6h    |
| [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased)   | t5-v1.1 | 512           | 0,76     | 1,07     | 64         | 1      | 1120000 | 0.1     | adafactor | 5e-3 | 86 13h   |

The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.

|                                                                                | model   | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
| [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test)   | t5-v1.1 | 1024      | 96         | 34,8   | 13,6   | 25,2   | 32,1      | 79           | 6      | 64         | 26916 | 2h 40m   |
| [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024      | 96         | 34,4   | 13,6   | 25,3   | 31,7      | 81           | 5      | 16         | 89720 | 11h      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
language:
- nl
datasets:
- yhavinga/mc4_nl_cleaned
- ml6team/cnn_dailymail_nl
tags:
- seq2seq
- lm-head
license: apache-2.0
inference: false
---

# T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱

This model is [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)

For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!

Rouge scores for this model are listed below.

## Tokenizer

* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

All models listed below are trained on of the `full` configuration (39B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.

* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
  Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
* The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
  with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
  base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
* The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
  Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
  be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
  The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
  base model is probably the better choice.

|                                                                                | model   | train seq len | acc      | loss     | batch size | epochs | steps   | dropout | optim     | lr   | duration |
|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
| [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch)                           | T5      | 512           | 0,70     | 1,38     | 128        | 1      | 528481  | 0.1     | adafactor | 5e-3 | 2d 9h    |
| [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024          | 0,73     | 1,20     | 64         | 2      | 1014525 | 0.0     | adafactor | 5e-3 | 5d 5h    |
| [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased)     | t5-v1.1 | 1024          | **0,78** | **0,96** | 64         | 2      | 1210000 | 0.0     | adafactor | 5e-3 | 6d 6h    |
| [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased)   | t5-v1.1 | 512           | 0,76     | 1,07     | 64         | 1      | 1120000 | 0.1     | adafactor | 5e-3 | 86 13h   |

The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.

|                                                                                | model   | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
| [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test)   | t5-v1.1 | 1024      | 96         | 34,8   | 13,6   | 25,2   | 32,1      | 79           | 6      | 64         | 26916 | 2h 40m   |
| [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024      | 96         | 34,4   | 13,6   | 25,3   | 31,7      | 81           | 5      | 16         | 89720 | 11h      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
language:
- nl
datasets:
- yhavinga/mc4_nl_cleaned
tags:
- seq2seq
- lm-head
license: apache-2.0
inference: false
---

# T5-base pre-trained on cleaned Dutch mC4 🇳🇱

A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) v1.1 base model pre-trained from scratch on [Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned). This model achieves an evaluation accuracy of 0,78 and loss 0,96.

* Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off.
* For a fine-tuned version for summarization, see [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test).
* For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
* T5 paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)

![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)

## Tokenizer

* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

All models listed below are trained on of the `full` configuration (39B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.

* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
  Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
* The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
  with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
  base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
* The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
  Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
  be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
  The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
  base model is probably the better choice.

|                                                                                | model   | train seq len | acc      | loss     | batch size | epochs | steps   | dropout | optim     | lr   | duration |
|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
| [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch)                           | T5      | 512           | 0,70     | 1,38     | 128        | 1      | 528481  | 0.1     | adafactor | 5e-3 | 2d 9h    |
| [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024          | 0,73     | 1,20     | 64         | 2      | 1014525 | 0.0     | adafactor | 5e-3 | 5d 5h    |
| [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased)     | t5-v1.1 | 1024          | **0,78** | **0,96** | 64         | 2      | 1210000 | 0.0     | adafactor | 5e-3 | 6d 6h    |
| [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased)   | t5-v1.1 | 512           | 0,76     | 1,07     | 64         | 1      | 1120000 | 0.1     | adafactor | 5e-3 | 86 13h   |

The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.

|                                                                                | model   | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
| [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test)   | t5-v1.1 | 1024      | 96         | 34,8   | 13,6   | 25,2   | 32,1      | 79           | 6      | 64         | 26916 | 2h 40m   |
| [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024      | 96         | 34,4   | 13,6   | 25,3   | 31,7      | 81           | 5      | 16         | 89720 | 11h      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-medium
- gpt2
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT-Neo 125M pre-trained on cleaned Dutch mC4 🇳🇱

A GPT-Neo small model (125M paramters) trained from scratch on Dutch, with perplexity 20.9 on cleaned Dutch mC4.

## How To Use

You can use this GPT-Neo model directly with a pipeline for text generation.

```python
MODEL_DIR='yhavinga/gpt-neo-125M-dutch'
from transformers import pipeline, GPT2Tokenizer, GPTNeoForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPTNeoForCausalLM.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)

generated_text = generator('Wetenschappers verbonden aan de Katholieke Universiteit', max_length=256, do_sample=True, top_k=50, top_p=0.95, temperature=0.7, no_repeat_ngram_size=2))
```

*"Wetenschappers verbonden aan de Katholieke Universiteit van Nijmegen" - "hebben ontdekt dat de genen die een mens heeft, een enorme invloed hebben op het DNA van zijn lichaam.
Cellen kunnen zich beter binden aan het DNA dan andere soorten cellen. De genen die de cellen maken, zijn bepalend voor de groei van de cel.
Het DNA van een mens is niet alleen informatiedrager, maar ook een bouwstof voor het DNA. Het wordt gevonden in de genen van een cel. Als er op een cel een cel"*

## Tokenizer

* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

This model was trained on of the `full` configuration (33B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except

  * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
  * Sentences with less than 3 words are removed
  * Sentences with a word of more than 1000 characters are removed
  * Documents with less than 5 sentences are removed
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
    "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

## Models

TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.

* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.

|                                                                                | model   | params | train seq len | ppl  | loss | batch size | epochs | steps           | optim     | lr     | duration | config    |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M   | 512           | 20.9 | 3.04 | 128        | 1      | 190000/558608          | adafactor | 2.4e-3 | 1d 12h   | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch)   | gpt2    | 345M   | 512           | 15.1 | 2.71 | 128        | 1      | 320000/520502   | adafactor | 8e-4   | 7d 2h    | full      |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch)     | gpt2    | 762M   | 512           | 15.1 | 2.72 | 32         | 1      | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h   | large     |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B   | 512           | 16.0 | 2.77 | 16         | 1      | 960000/3049896  | adafactor | 5e-4   | 7d 11h   | full      |

## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)


---
tags:
- summarization
language:
- dutch
datasets:
- xsum_nl
widget:
- text: "Onderzoekers ontdekten dat vier van de vijf kinderen in Engeland die op school lunches hadden gegeten, op school voedsel hadden geprobeerd dat ze thuis niet hadden geprobeerd.De helft van de ondervraagde ouders zei dat hun kinderen hadden gevraagd om voedsel dat ze op school hadden gegeten om thuis te worden gekookt.De enquête, van ongeveer 1.000 ouders, vond dat de meest populaire groenten wortelen, suikermaïs en erwten waren.Aubergine, kikkererwten en spinazie waren een van de minst populaire.Van de ondervraagde ouders, 628 hadden kinderen die lunches op school aten. (% duidt op een deel van de ouders die zeiden dat hun kind elke groente zou eten) England's School Food Trust gaf opdracht tot het onderzoek na een onderzoek door de Mumsnet-website suggereerde dat sommige ouders hun kinderen lunchpakket gaven omdat ze dachten dat ze te kieskeurig waren om iets anders te eten. \"Schoolmaaltijden kunnen een geweldige manier zijn om ouders te helpen hun kinderen aan te moedigen om nieuw voedsel te proberen en om de verscheidenheid van voedsel in hun dieet te verhogen. \"Mumsnet medeoprichter, Carrie Longton, zei: \"Het krijgen van kinderen om gezond te eten is de droom van elke ouder, maar maaltijdtijden thuis kan vaak een slagveld en emotioneel geladen zijn. \"Vanuit Mumsnetters' ervaring lijkt het erop dat eenmaal op school is er een verlangen om in te passen bij iedereen anders en zelfs een aantal positieve peer pressure om op te scheppen over de verscheidenheid van wat voedsel je kunt eten. \"Schoolmaaltijden zijn ook verplaatst op nogal een beetje van toen Mumsnetters op school waren, met gezondere opties en meer afwisseling. \"Schoolmaaltijden in Engeland moeten nu voldoen aan strenge voedingsrichtlijnen.Ongeveer vier op de tien basisschoolkinderen in Engeland eten nu schoollunches, iets meer dan op middelbare scholen.Meer kinderen in Schotland eten schoollunches - ongeveer 46%.Het onderzoek werd online uitgevoerd tussen 26 februari en 5 maart onder een panel van ouders die ten minste één kind op school hadden van 4-17 jaar oud."
- text: "Het Londense trio staat klaar voor de beste Britse act en beste album, evenals voor twee nominaties in de beste song categorie. \"We kregen te horen zoals vanmorgen 'Oh I think you're genomineerd',\" zei Dappy. \"En ik was als 'Oh yeah, what one?' En nu zijn we genomineerd voor vier awards. Ik bedoel, wow! \"Bandmate Fazer voegde eraan toe: \"We dachten dat het het beste van ons was om met iedereen naar beneden te komen en hallo te zeggen tegen de camera's.En nu vinden we dat we vier nominaties hebben. \"De band heeft twee shots bij de beste song prijs, het krijgen van het knikje voor hun Tyncy Stryder samenwerking nummer één, en single Strong Again.Their album Uncle B zal ook gaan tegen platen van Beyonce en Kany \"Aan het eind van de dag zijn we dankbaar om te zijn waar we zijn in onze carrières. \"Als het niet gebeurt dan gebeurt het niet - live om te vechten een andere dag en blijven maken albums en hits voor de fans. \"Dappy onthulde ook dat ze kunnen worden optreden live op de avond.De groep zal doen Nummer Een en ook een mogelijke uitlevering van de War Child single, I Got Soul.Het liefdadigheidslied is een re-working van The Killers' All These Things That I've Done en is ingesteld op artiesten als Chipmunk, Ironik en Pixie Lott.Dit jaar zal Mobos worden gehouden buiten Londen voor de eerste keer, in Glasgow op 30 september.N-Dubz zei dat ze op zoek waren naar optredens voor hun Schotse fans en bogen over hun recente shows ten noorden van de Londense We hebben Aberdeen ongeveer drie of vier maanden geleden gedaan - we hebben die show daar verbrijzeld! Overal waar we heen gaan slaan we hem in elkaar!\""
---

# mt5-base-mixednews-nl

mt5-base finetuned on three mixed news sources:

 1. CNN DM translated to Dutch with MarianMT.
 2. XSUM translated to Dutch with MarianMt.
 3. News article summaries distilled from the nu.nl website.

Config:

 * Learning rate 1e-3
 * Trained for one epoch
 * Max source length 1024
 * Max target length 142
 * Min target length 75

Scores:

 * rouge1 28.8482
 * rouge2 9.4584
 * rougeL 20.1697