modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
AnonymousSub/roberta-base_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"RobertaForQuestionAnswering"
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}
} | 6 | null | Access to model TLLM/bertbase-twcnstyle-entdebias is restricted and you are not in the authorized list. Visit https://huggingface.co/TLLM/bertbase-twcnstyle-entdebias to ask for access. |
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
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}
} | 3 | 2023-05-13T10:34:57Z | ---
license: bsd-3-clause
---
# CodeT5+ 220M
## Model description
[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks.
It is introduced in the paper:
[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution).
Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data.
Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture.
Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).
## How to use
This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5).
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = "Salesforce/codet5p-220m"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():<extra_id_0>", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=10)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# ==> print "Hello World"
```
## Pretraining data
This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code).
The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”).
Supported languages (9 in total) are as follows:
`c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.`
## Training procedure
This checkpoint is trained on the unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_.
Please refer to the paper for more details.
## Evaluation results
CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_.
Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g.,
8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4).
In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters.
Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode
Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details.
## BibTeX entry and citation info
```bibtex
@article{wang2023codet5plus,
title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
journal={arXiv preprint},
year={2023}
}
``` |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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}
} | 8 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
} | 2 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: student_offense_distilled_asda_gpt_ok
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. -->
# student_offense_distilled_asda_gpt_ok
This model is a fine-tuned version of [racai/distilbert-base-romanian-cased](https://huggingface.co/racai/distilbert-base-romanian-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.7934
- F1: 0.7943
## 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: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3887 | 1.0 | 14934 | 0.4498 | 0.7468 | 0.7514 |
| 0.336 | 2.0 | 29868 | 0.4083 | 0.7854 | 0.7862 |
| 0.2658 | 3.0 | 44802 | 0.3834 | 0.7870 | 0.7885 |
| 0.2667 | 4.0 | 59736 | 0.3738 | 0.7998 | 0.8004 |
| 0.2466 | 5.0 | 74670 | 0.3707 | 0.7934 | 0.7943 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"RobertaForQuestionAnswering"
],
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}
}
} | 2 | null | Trained in 4-bit on pygmalion-6b as POC
Uses the GPTeacher roleplay dataset.
```
INFO:Getting model ready...
INFO:Prepping for training...
INFO:Creating LoRA model...
INFO:Starting training...
{'loss': 12.5737, 'learning_rate': 0.0002926829268292683, 'epoch': 0.33}
{'loss': 8.5515, 'learning_rate': 0.0002560975609756097, 'epoch': 0.67}
{'loss': 7.5768, 'learning_rate': 0.0002195121951219512, 'epoch': 1.0}
{'loss': 6.9769, 'learning_rate': 0.00018292682926829266, 'epoch': 1.33}
{'loss': 6.6842, 'learning_rate': 0.00014634146341463414, 'epoch': 1.66}
{'loss': 6.3925, 'learning_rate': 0.0001097560975609756, 'epoch': 2.0}
{'loss': 6.041, 'learning_rate': 7.317073170731707e-05, 'epoch': 2.33}
{'loss': 5.6818, 'learning_rate': 3.6585365853658535e-05, 'epoch': 2.66}
{'loss': 5.4639, 'learning_rate': 0.0, 'epoch': 2.99}
{'train_runtime': 960.7748, 'train_samples_per_second': 6.005, 'train_steps_per_second': 0.047, 'train_loss': 7.326934729682074, 'epoch': 2.99}
INFO:LoRA training run is completed and saved.
INFO:Training complete!
```
I used the electricity so might as well post it. |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 24 | null | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_1.2B
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 1.2B_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --quantization float16 --output_dir converted/m2m_100_1.2b_ct2_float16
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 1.2B parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt
``` |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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}
} | 25 | 2023-05-13T13:01:28Z | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_418M
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 418M_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --output_dir converted/m2m_100_418m_ct2
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 418M parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt
``` |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 1 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8609120891618334
---
<!-- 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: 0.1400
- F1: 0.8609
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1584 | 0.8233 |
| 0.1252 | 2.0 | 1050 | 0.1384 | 0.8491 |
| 0.0811 | 3.0 | 1575 | 0.1400 | 0.8609 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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} | 23 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter-v1.0.0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.50 +/- 15.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
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} | 30 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for MYXGRL
## Model Description
- **Developed by:** BADMONK
- **Model type:** Dreambooth Model + Extracted LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
- **Parent Model:** ChilloutMix
# How to Get Started with the Model
Use the code below to get started with the model.
### MYXGRL ###
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 27 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: mlm-20230513-indobert-large-p1-002-pt1
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. -->
# mlm-20230513-indobert-large-p1-002-pt1
This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1513
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 284 | 3.7844 |
| 4.4953 | 2.0 | 568 | 3.0374 |
| 4.4953 | 3.0 | 852 | 2.7386 |
| 2.9063 | 4.0 | 1136 | 2.5432 |
| 2.9063 | 5.0 | 1420 | 2.3463 |
| 2.4449 | 6.0 | 1704 | 2.3084 |
| 2.4449 | 7.0 | 1988 | 2.2064 |
| 2.2361 | 8.0 | 2272 | 2.1498 |
| 2.1263 | 9.0 | 2556 | 2.1531 |
| 2.1263 | 10.0 | 2840 | 2.1542 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: aalonso-developer/vit-base-patch16-224-in21k-euroSat
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. -->
# aalonso-developer/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0212
- Train Accuracy: 0.9992
- Train Top-3-accuracy: 1.0000
- Validation Loss: 0.0613
- Validation Accuracy: 0.9864
- Validation Top-3-accuracy: 0.9998
- Epoch: 4
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3590, '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, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.4737 | 0.9429 | 0.9862 | 0.1568 | 0.9788 | 0.9993 | 0 |
| 0.0998 | 0.9878 | 0.9996 | 0.1010 | 0.9805 | 0.9993 | 1 |
| 0.0503 | 0.9946 | 0.9999 | 0.0720 | 0.9857 | 0.9998 | 2 |
| 0.0297 | 0.9978 | 1.0000 | 0.0606 | 0.9881 | 0.9995 | 3 |
| 0.0212 | 0.9992 | 1.0000 | 0.0613 | 0.9864 | 0.9998 | 4 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.11.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
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"bert",
"text-classification",
"transformers"
]
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} | 27 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: JoeyAndFriends/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/unsup-consert-papers | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | 2023-05-13T13:48:42Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 194.18 +/- 78.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: kids_phoneme_model
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. -->
# kids_phoneme_model
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: 3.0396
- Cer: 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 8.5987 | 0.1 | 5 | 7.0863 | 1.0 |
| 5.1336 | 0.2 | 10 | 5.3871 | 1.0 |
| 7.9056 | 0.3 | 15 | 4.7258 | 1.0 |
| 3.0144 | 0.4 | 20 | 3.3174 | 1.0 |
| 2.9792 | 0.5 | 25 | 3.1880 | 1.0 |
| 3.0351 | 0.6 | 30 | 3.1787 | 1.0 |
| 2.8973 | 0.7 | 35 | 3.1322 | 1.0 |
| 3.0372 | 0.8 | 40 | 3.0880 | 1.0 |
| 2.8416 | 0.9 | 45 | 3.0501 | 1.0 |
| 2.9727 | 1.0 | 50 | 3.0396 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3
|
Apisate/Discord-Ai-Bot | [
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"text-generation",
"transformers"
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} | 11 | null | This is GGML format quantised 4bit and 8bit model of Eric Hartford's 'uncensored' training of Wizard-Vicuna 13B.
This version is quantized using older version of llama.cpp (cf348a6) so it can be used with oobabooga/text-generation-webui. |
ArBert/bert-base-uncased-finetuned-ner-gmm | []
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} | 0 | null | ---
language:
- en
tags:
- feature-extraction
- sentence-similarity
datasets:
- biu-nlp/abstract-sim
widgets:
- sentence-similarity
- feature-extraction
---
A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Wikipedia. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
```python
from transformers import AutoTokenizer, AutoModel
import torch
from typing import List
from sklearn.metrics.pairwise import cosine_similarity
def load_finetuned_model():
sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence")
query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query")
tokenizer = AutoTokenizer.from_pretrained("biu-nlp/abstract-sim-sentence")
return tokenizer, query_encoder, sentence_encoder
def encode_batch(model, tokenizer, sentences: List[str], device: str):
input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
add_special_tokens=True).to(device)
features = model(**input_ids)[0]
features = torch.sum(features[:,1:,:] * input_ids["attention_mask"][:,1:].unsqueeze(-1), dim=1) / torch.clamp(torch.sum(input_ids["attention_mask"][:,1:], dim=1, keepdims=True), min=1e-9)
return features
```
Usage example:
```python
tokenizer, query_encoder, sentence_encoder = load_finetuned_model()
relevant_sentences = ["Fingersoft's parent company is the Finger Group.",
"WHIRC – a subsidiary company of Wright-Hennepin",
"CK Life Sciences International (Holdings) Inc. (), or CK Life Sciences, is a subsidiary of CK Hutchison Holdings",
"EM Microelectronic-Marin (subsidiary of The Swatch Group).",
"The company is currently a division of the corporate group Jam Industries.",
"Volt Technical Resources is a business unit of Volt Workforce Solutions, a subsidiary of Volt Information Sciences (currently trading over-the-counter as VISI.)."
]
irrelevant_sentences = ["The second company is deemed to be a subsidiary of the parent company.",
"The company has gone through more than one incarnation.",
"The company is owned by its employees.",
"Larger companies compete for market share by acquiring smaller companies that may own a particular market sector.",
"A parent company is a company that owns 51% or more voting stock in another firm (or subsidiary).",
"It is a holding company that provides services through its subsidiaries in the following areas: oil and gas, industrial and infrastructure, government and power.",
"RXVT Technologies is no longer a subsidiary of the parent company."
]
all_sentences = relevant_sentences + irrelevant_sentences
query = "<query>: A company is a part of a larger company."
embeddings = encode_batch(sentence_encoder, tokenizer, all_sentences, "cpu").detach().cpu().numpy()
query_embedding = encode_batch(query_encoder, tokenizer, [query], "cpu").detach().cpu().numpy()
sims = cosine_similarity(query_embedding, embeddings)[0]
sentences_sims = list(zip(all_sentences, sims))
sentences_sims.sort(key=lambda x: x[1], reverse=True)
for s, sim in sentences_sims:
print(s, sim)
```
Expected output:
```
WHIRC – a subsidiary company of Wright-Hennepin 0.9396286
EM Microelectronic-Marin (subsidiary of The Swatch Group). 0.93929046
Fingersoft's parent company is the Finger Group. 0.936247
CK Life Sciences International (Holdings) Inc. (), or CK Life Sciences, is a subsidiary of CK Hutchison Holdings 0.9350312
The company is currently a division of the corporate group Jam Industries. 0.9273489
Volt Technical Resources is a business unit of Volt Workforce Solutions, a subsidiary of Volt Information Sciences (currently trading over-the-counter as VISI.). 0.9005086
The second company is deemed to be a subsidiary of the parent company. 0.6723645
It is a holding company that provides services through its subsidiaries in the following areas: oil and gas, industrial and infrastructure, government and power. 0.60081375
A parent company is a company that owns 51% or more voting stock in another firm (or subsidiary). 0.59490484
The company is owned by its employees. 0.55286574
RXVT Technologies is no longer a subsidiary of the parent company. 0.4321953
The company has gone through more than one incarnation. 0.38889483
Larger companies compete for market share by acquiring smaller companies that may own a particular market sector. 0.25472647
``` |
Araby/Arabic-TTS | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: islamic_qa
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. -->
# islamic_qa
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1717
## 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: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 11 | 3.1441 |
| No log | 2.0 | 22 | 2.9286 |
| No log | 3.0 | 33 | 2.7457 |
| No log | 4.0 | 44 | 2.5833 |
| No log | 5.0 | 55 | 2.4321 |
| No log | 6.0 | 66 | 2.2999 |
| No log | 7.0 | 77 | 2.1775 |
| No log | 8.0 | 88 | 2.0746 |
| No log | 9.0 | 99 | 1.9797 |
| No log | 10.0 | 110 | 1.8859 |
| No log | 11.0 | 121 | 1.8029 |
| No log | 12.0 | 132 | 1.7232 |
| No log | 13.0 | 143 | 1.6503 |
| No log | 14.0 | 154 | 1.5891 |
| No log | 15.0 | 165 | 1.5228 |
| No log | 16.0 | 176 | 1.4632 |
| No log | 17.0 | 187 | 1.4108 |
| No log | 18.0 | 198 | 1.3575 |
| No log | 19.0 | 209 | 1.3103 |
| No log | 20.0 | 220 | 1.2638 |
| No log | 21.0 | 231 | 1.2153 |
| No log | 22.0 | 242 | 1.1701 |
| No log | 23.0 | 253 | 1.1310 |
| No log | 24.0 | 264 | 1.0866 |
| No log | 25.0 | 275 | 1.0509 |
| No log | 26.0 | 286 | 1.0171 |
| No log | 27.0 | 297 | 0.9829 |
| No log | 28.0 | 308 | 0.9542 |
| No log | 29.0 | 319 | 0.9152 |
| No log | 30.0 | 330 | 0.8851 |
| No log | 31.0 | 341 | 0.8544 |
| No log | 32.0 | 352 | 0.8278 |
| No log | 33.0 | 363 | 0.8037 |
| No log | 34.0 | 374 | 0.7745 |
| No log | 35.0 | 385 | 0.7502 |
| No log | 36.0 | 396 | 0.7238 |
| No log | 37.0 | 407 | 0.7018 |
| No log | 38.0 | 418 | 0.6780 |
| No log | 39.0 | 429 | 0.6555 |
| No log | 40.0 | 440 | 0.6354 |
| No log | 41.0 | 451 | 0.6164 |
| No log | 42.0 | 462 | 0.5978 |
| No log | 43.0 | 473 | 0.5804 |
| No log | 44.0 | 484 | 0.5567 |
| No log | 45.0 | 495 | 0.5464 |
| 1.5905 | 46.0 | 506 | 0.5234 |
| 1.5905 | 47.0 | 517 | 0.5087 |
| 1.5905 | 48.0 | 528 | 0.4961 |
| 1.5905 | 49.0 | 539 | 0.4807 |
| 1.5905 | 50.0 | 550 | 0.4663 |
| 1.5905 | 51.0 | 561 | 0.4514 |
| 1.5905 | 52.0 | 572 | 0.4411 |
| 1.5905 | 53.0 | 583 | 0.4253 |
| 1.5905 | 54.0 | 594 | 0.4170 |
| 1.5905 | 55.0 | 605 | 0.4028 |
| 1.5905 | 56.0 | 616 | 0.3945 |
| 1.5905 | 57.0 | 627 | 0.3828 |
| 1.5905 | 58.0 | 638 | 0.3736 |
| 1.5905 | 59.0 | 649 | 0.3638 |
| 1.5905 | 60.0 | 660 | 0.3530 |
| 1.5905 | 61.0 | 671 | 0.3476 |
| 1.5905 | 62.0 | 682 | 0.3373 |
| 1.5905 | 63.0 | 693 | 0.3260 |
| 1.5905 | 64.0 | 704 | 0.3213 |
| 1.5905 | 65.0 | 715 | 0.3156 |
| 1.5905 | 66.0 | 726 | 0.3069 |
| 1.5905 | 67.0 | 737 | 0.3019 |
| 1.5905 | 68.0 | 748 | 0.2943 |
| 1.5905 | 69.0 | 759 | 0.2877 |
| 1.5905 | 70.0 | 770 | 0.2798 |
| 1.5905 | 71.0 | 781 | 0.2765 |
| 1.5905 | 72.0 | 792 | 0.2719 |
| 1.5905 | 73.0 | 803 | 0.2653 |
| 1.5905 | 74.0 | 814 | 0.2602 |
| 1.5905 | 75.0 | 825 | 0.2567 |
| 1.5905 | 76.0 | 836 | 0.2530 |
| 1.5905 | 77.0 | 847 | 0.2468 |
| 1.5905 | 78.0 | 858 | 0.2413 |
| 1.5905 | 79.0 | 869 | 0.2376 |
| 1.5905 | 80.0 | 880 | 0.2347 |
| 1.5905 | 81.0 | 891 | 0.2301 |
| 1.5905 | 82.0 | 902 | 0.2269 |
| 1.5905 | 83.0 | 913 | 0.2240 |
| 1.5905 | 84.0 | 924 | 0.2211 |
| 1.5905 | 85.0 | 935 | 0.2170 |
| 1.5905 | 86.0 | 946 | 0.2147 |
| 1.5905 | 87.0 | 957 | 0.2114 |
| 1.5905 | 88.0 | 968 | 0.2090 |
| 1.5905 | 89.0 | 979 | 0.2076 |
| 1.5905 | 90.0 | 990 | 0.2050 |
| 0.5451 | 91.0 | 1001 | 0.2020 |
| 0.5451 | 92.0 | 1012 | 0.1991 |
| 0.5451 | 93.0 | 1023 | 0.1981 |
| 0.5451 | 94.0 | 1034 | 0.1959 |
| 0.5451 | 95.0 | 1045 | 0.1929 |
| 0.5451 | 96.0 | 1056 | 0.1911 |
| 0.5451 | 97.0 | 1067 | 0.1903 |
| 0.5451 | 98.0 | 1078 | 0.1882 |
| 0.5451 | 99.0 | 1089 | 0.1865 |
| 0.5451 | 100.0 | 1100 | 0.1854 |
| 0.5451 | 101.0 | 1111 | 0.1846 |
| 0.5451 | 102.0 | 1122 | 0.1829 |
| 0.5451 | 103.0 | 1133 | 0.1815 |
| 0.5451 | 104.0 | 1144 | 0.1806 |
| 0.5451 | 105.0 | 1155 | 0.1797 |
| 0.5451 | 106.0 | 1166 | 0.1784 |
| 0.5451 | 107.0 | 1177 | 0.1767 |
| 0.5451 | 108.0 | 1188 | 0.1760 |
| 0.5451 | 109.0 | 1199 | 0.1756 |
| 0.5451 | 110.0 | 1210 | 0.1748 |
| 0.5451 | 111.0 | 1221 | 0.1743 |
| 0.5451 | 112.0 | 1232 | 0.1737 |
| 0.5451 | 113.0 | 1243 | 0.1732 |
| 0.5451 | 114.0 | 1254 | 0.1727 |
| 0.5451 | 115.0 | 1265 | 0.1723 |
| 0.5451 | 116.0 | 1276 | 0.1720 |
| 0.5451 | 117.0 | 1287 | 0.1718 |
| 0.5451 | 118.0 | 1298 | 0.1717 |
| 0.5451 | 119.0 | 1309 | 0.1717 |
| 0.5451 | 120.0 | 1320 | 0.1717 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArashEsk95/bert-base-uncased-finetuned-stsb | []
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} | 0 | null | ---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-test
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. -->
# segformer-b0-finetuned-segments-test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bilal01/stamp-verification-test 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: 6e-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: 5
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArjunKadya/HuggingFace | []
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: indian_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9436769394261424
---
<!-- 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. -->
# finetuned-indian-food
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 indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2342
- Accuracy: 0.9437
## 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: 0.0002
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1912 | 0.3 | 100 | 0.9449 | 0.8470 |
| 0.7387 | 0.6 | 200 | 0.5696 | 0.8969 |
| 0.6616 | 0.9 | 300 | 0.4609 | 0.8969 |
| 0.4093 | 1.2 | 400 | 0.4250 | 0.8937 |
| 0.3707 | 1.5 | 500 | 0.3226 | 0.9182 |
| 0.3725 | 1.8 | 600 | 0.3941 | 0.8895 |
| 0.2317 | 2.1 | 700 | 0.2870 | 0.9309 |
| 0.256 | 2.4 | 800 | 0.2753 | 0.9267 |
| 0.2077 | 2.7 | 900 | 0.2698 | 0.9341 |
| 0.1442 | 3.0 | 1000 | 0.2775 | 0.9288 |
| 0.2138 | 3.3 | 1100 | 0.2342 | 0.9437 |
| 0.1862 | 3.6 | 1200 | 0.2412 | 0.9394 |
| 0.142 | 3.9 | 1300 | 0.2347 | 0.9437 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Arpita/opus-mt-en-ro-finetuned-syn-to-react | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Arpita/opus-mt-en-ro-finetuned-synthon-to-reactant | []
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} | 0 | null | ---
license: openrail
tags:
- document-image-binarization
- image-segmentation
- generated_from_trainer
model-index:
- name: binarization-segformer-b3
results: []
pipeline_tag: image-segmentation
---
<!-- 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. -->
# binarization-segformer-b3
This model is a fine-tuned version of [nvidia/segformer-b3](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024)
on the same ensemble of 13 datasets as the [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf) work publicly available
in their GitHub [repository](https://github.com/Leedeng/SauvolaNet#datasets).
It achieves the following results on the evaluation set on DIBCO metrics:
- loss: 0.1017
- F-measure: 0.9776
- pseudo F-measure: 0.9531
- PSNR: 14.5040
- DRD: 5.3749
with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.
For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
**Warning:** This model only accepts images with a resolution of 640 due to GPU compute constraints on Colab free tier during training.
## Model description
This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
## 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: 10
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 50
### Training results
| training loss | epoch | step | validation loss | F-measure | pseudo F-measure | PSNR | DRD |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:-------:|:--------:|
| 0.6667 | 1.03 | 10 | 0.6683 | 0.7127 | 0.6831 | 4.8248 | 107.2894 |
| 0.6371 | 2.05 | 20 | 0.6390 | 0.8173 | 0.7360 | 6.1079 | 69.7770 |
| 0.587 | 3.08 | 30 | 0.5652 | 0.8934 | 0.8187 | 7.9143 | 40.5464 |
| 0.5288 | 4.1 | 40 | 0.4926 | 0.9240 | 0.8554 | 9.2247 | 27.4220 |
| 0.4601 | 5.13 | 50 | 0.4244 | 0.9490 | 0.8944 | 10.8830 | 16.8051 |
| 0.3864 | 6.15 | 60 | 0.3446 | 0.9638 | 0.9218 | 12.3460 | 10.6997 |
| 0.3331 | 7.18 | 70 | 0.3055 | 0.9693 | 0.9317 | 13.0531 | 8.5298 |
| 0.2821 | 8.21 | 80 | 0.2512 | 0.9736 | 0.9427 | 13.6929 | 6.8343 |
| 0.2392 | 9.23 | 90 | 0.2112 | 0.9744 | 0.9462 | 13.8825 | 6.4094 |
| 0.2126 | 10.26 | 100 | 0.1948 | 0.9743 | 0.9433 | 13.8424 | 6.5637 |
| 0.1889 | 11.28 | 110 | 0.1710 | 0.9749 | 0.9499 | 13.9784 | 6.1757 |
| 0.1662 | 12.31 | 120 | 0.1604 | 0.9753 | 0.9495 | 14.0450 | 6.0929 |
| 0.1506 | 13.33 | 130 | 0.1451 | 0.9750 | 0.9550 | 14.0028 | 6.1031 |
| 0.1359 | 14.36 | 140 | 0.1362 | 0.9759 | 0.9501 | 14.1383 | 5.9699 |
| 0.1321 | 15.38 | 150 | 0.1351 | 0.9761 | 0.9485 | 14.1907 | 5.9045 |
| 0.1283 | 16.41 | 160 | 0.1266 | 0.9758 | 0.9541 | 14.1515 | 5.8287 |
| 0.1198 | 17.44 | 170 | 0.1232 | 0.9763 | 0.9535 | 14.2411 | 5.7300 |
| 0.1151 | 18.46 | 180 | 0.1232 | 0.9765 | 0.9482 | 14.2788 | 5.8266 |
| 0.1146 | 19.49 | 190 | 0.1183 | 0.9764 | 0.9530 | 14.2363 | 5.7922 |
| 0.1027 | 20.51 | 200 | 0.1162 | 0.9765 | 0.9535 | 14.2867 | 5.6246 |
| 0.1051 | 21.54 | 210 | 0.1146 | 0.9766 | 0.9551 | 14.2963 | 5.6159 |
| 0.1095 | 22.56 | 220 | 0.1159 | 0.9767 | 0.9497 | 14.3153 | 5.8966 |
| 0.1076 | 23.59 | 230 | 0.1106 | 0.9768 | 0.9533 | 14.3267 | 5.6436 |
| 0.1006 | 24.62 | 240 | 0.1113 | 0.9769 | 0.9483 | 14.3683 | 5.6679 |
| 0.1077 | 25.64 | 250 | 0.1086 | 0.9770 | 0.9544 | 14.3843 | 5.4949 |
| 0.0966 | 26.67 | 260 | 0.1077 | 0.9770 | 0.9553 | 14.3660 | 5.5337 |
| 0.0958 | 27.69 | 270 | 0.1071 | 0.9773 | 0.9529 | 14.4405 | 5.4582 |
| 0.0984 | 28.72 | 280 | 0.1055 | 0.9772 | 0.9536 | 14.4405 | 5.4365 |
| 0.0936 | 29.74 | 290 | 0.1056 | 0.9774 | 0.9528 | 14.4634 | 5.4066 |
| 0.0958 | 30.77 | 300 | 0.1049 | 0.9772 | 0.9544 | 14.4138 | 5.4854 |
| 0.0896 | 31.79 | 310 | 0.1043 | 0.9774 | 0.9533 | 14.4593 | 5.4351 |
| 0.0973 | 32.82 | 320 | 0.1035 | 0.9774 | 0.9528 | 14.4633 | 5.4430 |
| 0.0943 | 33.85 | 330 | 0.1033 | 0.9775 | 0.9527 | 14.4809 | 5.4193 |
| 0.0956 | 34.87 | 340 | 0.1026 | 0.9774 | 0.9543 | 14.4576 | 5.4070 |
| 0.0936 | 35.9 | 350 | 0.1031 | 0.9775 | 0.9531 | 14.4827 | 5.4137 |
| 0.0937 | 36.92 | 360 | 0.1028 | 0.9773 | 0.9551 | 14.4420 | 5.4084 |
| 0.0952 | 37.95 | 370 | 0.1023 | 0.9775 | 0.9541 | 14.4809 | 5.3769 |
| 0.0952 | 38.97 | 380 | 0.1023 | 0.9776 | 0.9525 | 14.5086 | 5.3839 |
| 0.0948 | 40.0 | 390 | 0.1020 | 0.9774 | 0.9546 | 14.4667 | 5.3800 |
| 0.0931 | 41.03 | 400 | 0.1020 | 0.9776 | 0.9534 | 14.5043 | 5.3728 |
| 0.0906 | 42.05 | 410 | 0.1023 | 0.9774 | 0.9544 | 14.4771 | 5.3773 |
| 0.0974 | 43.08 | 420 | 0.1019 | 0.9776 | 0.9536 | 14.5024 | 5.3718 |
| 0.0908 | 44.1 | 430 | 0.1025 | 0.9776 | 0.9536 | 14.4995 | 5.3730 |
| 0.0935 | 45.13 | 440 | 0.1024 | 0.9775 | 0.9537 | 14.4978 | 5.3715 |
| 0.0927 | 46.15 | 450 | 0.1017 | 0.9776 | 0.9531 | 14.5040 | 5.3749 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3 |
ArthurBaia/bert-base-portuguese-cased-finetuned-squad | []
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} | 0 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Aruden/DialoGPT-medium-harrypotterall | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
}
} | 6 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
ArvinZhuang/BiTAG-t5-large | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
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"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2023-05-13T16:35:40Z | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 103 | 2023-05-13T16:37:49Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Ashagi/Ashvx | []
| null | {
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}
} | 0 | 2023-05-13T16:40:34Z | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
AshiNLP/Bert_model | []
| null | {
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}
} | 0 | null | Access to model Wilhelmus/GraphicalDiffusion is restricted and you are not in the authorized list. Visit https://huggingface.co/Wilhelmus/GraphicalDiffusion to ask for access. |
Atchuth/DialoGPT-small-MBOT | []
| null | {
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}
} | 0 | 2023-05-13T16:55:33Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: peft-prefix-jul
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. -->
# peft-prefix-jul
This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- Loc: {'precision': 0.711864406779661, 'recall': 0.7777777777777778, 'f1': 0.7433628318584071, 'number': 216}
- Misc: {'precision': 0.6956521739130435, 'recall': 0.4, 'f1': 0.507936507936508, 'number': 40}
- Org: {'precision': 0.8097560975609757, 'recall': 0.83, 'f1': 0.8197530864197532, 'number': 200}
- Per: {'precision': 0.8260869565217391, 'recall': 0.7755102040816326, 'f1': 0.8, 'number': 196}
- Overall Precision: 0.7747
- Overall Recall: 0.7699
- Overall F1: 0.7723
- Overall Accuracy: 0.9822
## 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: 0.0002
- 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: 10
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ateeb/EmotionDetector | [
"pytorch",
"funnel",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"FunnelForSequenceClassification"
],
"model_type": "funnel",
"task_specific_params": {
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},
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}
} | 32 | 2023-05-13T17:01:35Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - RenauxLouis/monet-test
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the github-smaller-dataset dataset. You can find some example images in the following.
|
Augustvember/WokkaBot9 | []
| null | {
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}
} | 0 | null | ---
language:
- ar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper_large_v2_arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ar
split: test
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 12.773732872855585
---
<!-- 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_arabic
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.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Wer: 12.7737
## 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: 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: 500
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0259 | 0.83 | 500 | 0.2119 | 12.7737 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Augustvember/WokkaBotF | []
| null | {
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} | 0 | null | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: kasunw/ppo-SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Augustvember/wokka4 | [
"conversational"
]
| conversational | {
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}
} | 0 | 2023-05-13T17:43:46Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="parallelq/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Aurora/asdawd | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- customized
model-index:
- name: h34
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. -->
# h34
This model is a fine-tuned version of [pinkmanlove/llama-7b-hf](https://huggingface.co/pinkmanlove/llama-7b-hf) on the customized 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
Ayham/bertgpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 4 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64078/tenjouin-asuka-yu-gi-oh |
Ayham/distilbert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 11 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64069/kirika-towa-alma-shining-resonance |
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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} | 6 | null | ---
license: openrail
datasets:
- OpenAssistant/oasst1
language:
- en
metrics:
- accuracy
library_name: nemo
pipeline_tag: text-to-image
tags:
- art
---
# Model Card for Model ID
<!-- Provide a quick summalleymixary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 8 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64271/natashahonkai-star-rail |
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 14 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64385/swiftsure-azur-lane-midsummer-special-service-swimsuit |
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 12 | 2023-05-13T18:16:58Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64841/miorine-rembran-or-the-witch-from-mercury |
Ayham/roberta_gpt2_new_max64_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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},
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},
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}
} | 4 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/6100/tifa-lockhart-lessall-outfitsgreater-lora |
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 31 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/65131/yatogami-tohka-date-a-live |
Ayham/roberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 6 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/60186/chosen-impasto-mix |
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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"task_specific_params": {
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}
} | 10 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 399.40 +/- 145.88
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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}
} | 12 | 2023-05-13T18:38:46Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bastienm/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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},
"summarization": {
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8676470588235294
- name: F1
type: f1
value: 0.9093959731543624
---
<!-- 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-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6265
- Accuracy: 0.8676
- F1: 0.9094
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3924 | 0.8554 | 0.9015 |
| No log | 2.0 | 460 | 0.3575 | 0.875 | 0.9128 |
| 0.3857 | 3.0 | 690 | 0.6265 | 0.8676 | 0.9094 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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} | 14 | 2023-05-13T19:04:01Z | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Barytes/hellohf | [
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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}
} | 2 | 2023-05-13T20:52:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dummy-fashion-classification
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. -->
# dummy-fashion-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1122
- Accuracy: 0.9665
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3331 | 1.0 | 294 | 0.1725 | 0.9519 |
| 0.296 | 2.0 | 588 | 0.1323 | 0.9591 |
| 0.2484 | 3.0 | 882 | 0.1122 | 0.9665 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BatuhanYilmaz/bert-finetuned-nerxD | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### WendiTrainedDelibarate Dreambooth model trained by MVPWendi 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)
Sample pictures of this concept:
|
BenDavis71/GPT-2-Finetuning-AIRaid | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 10 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 20.44 +/- 1.69
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r NathanS-HuggingFace/VizDoom-Health-Gathering
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.lib.python3.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=VizDoom-Health-Gathering
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.lib.python3.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=VizDoom-Health-Gathering --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
BigSalmon/Flowberta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 13 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-l3-bert-latest
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. -->
# finetuning-l3-bert-latest
This model is a fine-tuned version of [l3cube-pune/telugu-bert](https://huggingface.co/l3cube-pune/telugu-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6283
- eval_accuracy: 0.7558
- eval_f1: 0.7529
- eval_runtime: 79.9067
- eval_samples_per_second: 51.61
- eval_steps_per_second: 6.458
- step: 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: 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
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/InformalToFormalLincoln20 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 8 | 2023-05-14T00:32:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_wikilingua_model2
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. -->
# my_wikilingua_model2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5821
- Rouge1: 0.2402
- Rouge2: 0.0747
- Rougel: 0.1991
- Rougelsum: 0.1993
- Gen Len: 18.82
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 100 | 2.6861 | 0.2284 | 0.0646 | 0.1832 | 0.183 | 18.9375 |
| No log | 2.0 | 200 | 2.6137 | 0.2343 | 0.0704 | 0.1919 | 0.1916 | 18.84 |
| No log | 3.0 | 300 | 2.5890 | 0.2384 | 0.0729 | 0.1967 | 0.1966 | 18.88 |
| No log | 4.0 | 400 | 2.5821 | 0.2402 | 0.0747 | 0.1991 | 0.1993 | 18.82 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/InformalToFormalLincolnDistilledGPT2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: viettel-xlm-roberta-base-finetune-viquad-model-2
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. -->
# viettel-xlm-roberta-base-finetune-viquad-model-2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2581
## 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
- 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_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.73 | 300 | 1.6542 |
| 2.5237 | 1.45 | 600 | 1.3745 |
| 2.5237 | 2.18 | 900 | 1.3041 |
| 1.2219 | 2.91 | 1200 | 1.2581 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 10 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9430473372781065
- name: Recall
type: recall
value: 0.9543413173652695
- name: F1
type: f1
value: 0.9486607142857143
- name: Accuracy
type: accuracy
value: 0.9579796264855688
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2188
- Precision: 0.9430
- Recall: 0.9543
- F1: 0.9487
- Accuracy: 0.9580
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0024 | 0.7392 | 0.7957 | 0.7664 | 0.8060 |
| 1.3949 | 3.12 | 500 | 0.5684 | 0.8330 | 0.8660 | 0.8492 | 0.8727 |
| 1.3949 | 4.69 | 750 | 0.3929 | 0.8931 | 0.9072 | 0.9001 | 0.9160 |
| 0.3964 | 6.25 | 1000 | 0.3312 | 0.9236 | 0.9326 | 0.9281 | 0.9321 |
| 0.3964 | 7.81 | 1250 | 0.2754 | 0.9275 | 0.9386 | 0.9330 | 0.9410 |
| 0.216 | 9.38 | 1500 | 0.2447 | 0.9328 | 0.9454 | 0.9390 | 0.9478 |
| 0.216 | 10.94 | 1750 | 0.2467 | 0.9363 | 0.9461 | 0.9412 | 0.9478 |
| 0.1534 | 12.5 | 2000 | 0.2300 | 0.9436 | 0.9521 | 0.9478 | 0.9537 |
| 0.1534 | 14.06 | 2250 | 0.2155 | 0.9459 | 0.9558 | 0.9509 | 0.9597 |
| 0.119 | 15.62 | 2500 | 0.2188 | 0.9430 | 0.9543 | 0.9487 | 0.9580 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bilz/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: phobert_shared-vietnews
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. -->
# phobert_shared-vietnews
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4872
- Rouge1: 47.252
- Rouge2: 12.3801
- Rougel: 27.9535
- Rougelsum: 31.1165
- Gen Len: 25.3494
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 4.1717 | 1.0 | 619 | 3.7817 | 46.287 | 10.7692 | 27.3606 | 30.4956 | 25.2656 |
| 3.6535 | 2.0 | 1239 | 3.5421 | 46.9278 | 11.7595 | 27.5206 | 30.7512 | 25.9089 |
| 3.434 | 3.0 | 1857 | 3.4872 | 47.252 | 12.3801 | 27.9535 | 31.1165 | 25.3494 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Binbin/test | []
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} | 0 | null | ---
license: cc-by-nc-sa-4.0
---
# Lighting ASR英文语音识别模型
## 环境依赖
```
- Python3.7+
- PyTorch 1.8.1+
- torchaudio
- soundfile
- librosa
```
## 安装[lighting-asr](https://github.com/gaochangfeng/lighting-asr.git)
### 下载源代码并添加到python路径
```
export PYTHONPATH=/path/to/lasrfolder/:$PYTHONPATH
```
### 源文件安装
```
git clone https://github.com/gaochangfeng/lighting-asr.git
cd lighting-asr
python setup.py install
```
### PIP 安装
```
pip install git+https://github.com/gaochangfeng/lighting-asr.git
```
## 使用
```
from lasr.process.asrprocess import ASRProcess
train_config="hparams.yaml"
decode_config="decode.yaml"
model_path="model.ckpt"
asrpipeline = ASRProcess(
train_config=train_config,
decode_config=decode_config,
model_path=model_path
)
token, text = asrpipeline("test.wav")
print(token)
print(text)
```
## 模型训练
参考https:https://github.com/gaochangfeng/lighting-asr.git
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 12 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -126.53 +/- 47.03
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Felix555/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 12 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: viettel-xlm-roberta-base-finetune-viquad-model-4
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. -->
# viettel-xlm-roberta-base-finetune-viquad-model-4
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2693
## 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
- 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: 200
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.73 | 300 | 1.7389 |
| 2.7394 | 1.45 | 600 | 1.3885 |
| 2.7394 | 2.18 | 900 | 1.3075 |
| 1.2501 | 2.91 | 1200 | 1.2693 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Bloodwarrior/Chikfalay | []
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} | 0 | null | BART MODEL #10 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM |
BlueGamerBeast/DialoGPT-small-Morgana | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | PEGASUS MODEL #5 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM |
BobBraico/bert-finetuned-ner | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: finetuned-facebook-bart-base
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. -->
# finetuned-facebook-bart-base
This model is a fine-tuned version of [../tmp/bart-abst-summarization](https://huggingface.co/../tmp/bart-abst-summarization) 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: 4
- eval_batch_size: 4
- 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
- num_epochs: 3.0
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.48 +/- 3.24
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Felix555/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Botjallu/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jokyere49/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Brayan/CNN_Brain_Tumor | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: viettel-xlm-roberta-base-finetune-viquad-model-5
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. -->
# viettel-xlm-roberta-base-finetune-viquad-model-5
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2642
## 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
- 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_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.73 | 300 | 1.6829 |
| 2.6038 | 1.45 | 600 | 1.3976 |
| 2.6038 | 2.18 | 900 | 1.3037 |
| 1.2138 | 2.91 | 1200 | 1.2309 |
| 0.9802 | 3.64 | 1500 | 1.2642 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Brykee/BrykeeBot | []
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} | 0 | null | ---
license: apache-2.0
pipeline_tag: text2text-generation
--- |
BumBelDumBel/TRUMP | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
]
| text-generation | {
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}
} | 5 | null | BART MODEL #11 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM |
BumBelDumBel/ZORK-AI-TEST | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
]
| text-generation | {
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} | 9 | null | BART MODEL #12 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM |
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
],
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}
} | 71 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.29 +/- 26.13
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
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"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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}
} | 132 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-active_passive
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-base-active_passive
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- Rouge1: 95.4132
- Rouge2: 92.1835
- Rougel: 95.2747
- Rougelsum: 95.2517
- Gen Len: 11.6716
- Exact Match: 75.5224
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Exact Match |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-----------:|
| 0.8403 | 1.0 | 2680 | 0.3439 | 84.2519 | 75.0962 | 83.0988 | 83.0654 | 11.3821 | 44.1791 |
| 0.3755 | 2.0 | 5360 | 0.2136 | 93.7164 | 88.9172 | 93.5505 | 93.5463 | 11.6522 | 68.3582 |
| 0.2587 | 3.0 | 8040 | 0.1703 | 94.8793 | 91.2347 | 94.6677 | 94.6651 | 11.6746 | 73.1343 |
| 0.1993 | 4.0 | 10720 | 0.1740 | 95.4366 | 92.1875 | 95.2486 | 95.2388 | 11.6687 | 75.3731 |
| 0.1763 | 5.0 | 13400 | 0.1959 | 95.4608 | 92.3592 | 95.3141 | 95.2889 | 11.6716 | 75.6716 |
| 0.1337 | 6.0 | 16080 | 0.1623 | 95.4132 | 92.1835 | 95.2747 | 95.2517 | 11.6716 | 75.5224 |
| 0.1152 | 7.0 | 18760 | 0.1748 | 95.5064 | 92.3455 | 95.3485 | 95.3306 | 11.6672 | 74.3284 |
| 0.1374 | 8.0 | 21440 | 0.1742 | 95.9602 | 93.3902 | 95.7887 | 95.7772 | 11.6955 | 76.7164 |
| 0.0942 | 9.0 | 24120 | 0.1743 | 96.0312 | 93.4795 | 95.8994 | 95.8892 | 11.6821 | 77.6119 |
| 0.077 | 10.0 | 26800 | 0.1780 | 95.9435 | 93.3477 | 95.783 | 95.7725 | 11.6836 | 77.6119 |
| 0.0554 | 11.0 | 29480 | 0.1967 | 96.0079 | 93.2747 | 95.8457 | 95.8357 | 11.6836 | 77.3134 |
| 0.0575 | 12.0 | 32160 | 0.1837 | 96.11 | 93.6604 | 95.9517 | 95.9382 | 11.7015 | 77.7612 |
| 0.0399 | 13.0 | 34840 | 0.1927 | 95.9004 | 93.2349 | 95.7718 | 95.7414 | 11.7075 | 77.1642 |
| 0.0501 | 14.0 | 37520 | 0.2058 | 95.9167 | 93.3185 | 95.7849 | 95.7705 | 11.7090 | 77.4627 |
| 0.0501 | 15.0 | 40200 | 0.2148 | 95.985 | 93.4129 | 95.8509 | 95.8453 | 11.6746 | 77.4627 |
| 0.0407 | 16.0 | 42880 | 0.2086 | 95.9699 | 93.4136 | 95.8351 | 95.8377 | 11.6731 | 77.6119 |
| 0.0412 | 17.0 | 45560 | 0.2130 | 95.9691 | 93.4489 | 95.8442 | 95.8363 | 11.6925 | 77.3134 |
| 0.0388 | 18.0 | 48240 | 0.2269 | 95.7669 | 93.0314 | 95.6346 | 95.6307 | 11.6925 | 77.3134 |
| 0.0208 | 19.0 | 50920 | 0.2413 | 96.0036 | 93.5595 | 95.8972 | 95.8947 | 11.6985 | 77.9104 |
| 0.0344 | 20.0 | 53600 | 0.2572 | 95.8866 | 93.351 | 95.7917 | 95.7935 | 11.6910 | 77.3134 |
| 0.0403 | 21.0 | 56280 | 0.2423 | 95.9904 | 93.5134 | 95.8618 | 95.8566 | 11.6970 | 77.9104 |
| 0.0298 | 22.0 | 58960 | 0.2569 | 95.7327 | 93.0561 | 95.5967 | 95.5909 | 11.6910 | 77.3134 |
| 0.0286 | 23.0 | 61640 | 0.2830 | 95.8656 | 93.2363 | 95.7428 | 95.7256 | 11.6836 | 77.1642 |
| 0.0219 | 24.0 | 64320 | 0.2733 | 95.6646 | 92.8942 | 95.4774 | 95.4861 | 11.6866 | 77.0149 |
| 0.0135 | 25.0 | 67000 | 0.2867 | 95.9215 | 93.3702 | 95.7757 | 95.7707 | 11.6746 | 77.4627 |
| 0.0195 | 26.0 | 69680 | 0.2764 | 95.905 | 93.4722 | 95.7875 | 95.7905 | 11.6985 | 77.6119 |
| 0.0161 | 27.0 | 72360 | 0.2936 | 96.0248 | 93.577 | 95.8972 | 95.8892 | 11.6910 | 77.7612 |
| 0.016 | 28.0 | 75040 | 0.2656 | 95.8823 | 93.2351 | 95.7332 | 95.717 | 11.6955 | 77.1642 |
| 0.007 | 29.0 | 77720 | 0.2579 | 95.9672 | 93.4164 | 95.8154 | 95.7959 | 11.6955 | 77.7612 |
| 0.0221 | 30.0 | 80400 | 0.2838 | 95.7522 | 93.0823 | 95.6266 | 95.6061 | 11.6970 | 77.0149 |
| 0.017 | 31.0 | 83080 | 0.2990 | 95.8869 | 93.3126 | 95.7469 | 95.755 | 11.6955 | 77.4627 |
| 0.0199 | 32.0 | 85760 | 0.3084 | 95.7885 | 93.1157 | 95.6569 | 95.6548 | 11.6940 | 77.3134 |
| 0.0088 | 33.0 | 88440 | 0.2883 | 95.8547 | 93.2583 | 95.7285 | 95.7085 | 11.7015 | 77.6119 |
| 0.0145 | 34.0 | 91120 | 0.3002 | 95.7988 | 93.215 | 95.6797 | 95.6743 | 11.7015 | 77.3134 |
| 0.0117 | 35.0 | 93800 | 0.3173 | 95.7973 | 93.2558 | 95.6875 | 95.6928 | 11.6925 | 77.3134 |
| 0.0103 | 36.0 | 96480 | 0.3283 | 95.7067 | 92.9859 | 95.5942 | 95.59 | 11.6940 | 76.8657 |
| 0.0185 | 37.0 | 99160 | 0.3191 | 95.9002 | 93.32 | 95.7707 | 95.7464 | 11.6970 | 77.4627 |
| 0.0145 | 38.0 | 101840 | 0.3350 | 95.77 | 93.1028 | 95.642 | 95.6276 | 11.7 | 77.3134 |
| 0.015 | 39.0 | 104520 | 0.3416 | 95.8064 | 93.2264 | 95.669 | 95.6659 | 11.7030 | 77.6119 |
| 0.008 | 40.0 | 107200 | 0.3322 | 95.8005 | 93.1618 | 95.6605 | 95.6595 | 11.7 | 77.3134 |
| 0.0065 | 41.0 | 109880 | 0.3312 | 95.8407 | 93.2715 | 95.7128 | 95.7058 | 11.7 | 77.6119 |
| 0.0067 | 42.0 | 112560 | 0.3412 | 95.8997 | 93.4001 | 95.7679 | 95.7675 | 11.6985 | 77.7612 |
| 0.0097 | 43.0 | 115240 | 0.3340 | 95.8249 | 93.2442 | 95.6857 | 95.6757 | 11.7015 | 77.4627 |
| 0.0093 | 44.0 | 117920 | 0.3443 | 95.8464 | 93.3354 | 95.7134 | 95.722 | 11.7 | 77.7612 |
| 0.0062 | 45.0 | 120600 | 0.3449 | 95.8265 | 93.2712 | 95.6944 | 95.7025 | 11.7030 | 77.6119 |
| 0.0083 | 46.0 | 123280 | 0.3445 | 95.9734 | 93.5331 | 95.8377 | 95.8351 | 11.7 | 78.0597 |
| 0.0081 | 47.0 | 125960 | 0.3426 | 95.9487 | 93.4918 | 95.8099 | 95.8051 | 11.7 | 78.0597 |
| 0.0109 | 48.0 | 128640 | 0.3420 | 95.9487 | 93.4918 | 95.8099 | 95.8051 | 11.7 | 78.0597 |
| 0.0064 | 49.0 | 131320 | 0.3396 | 95.8866 | 93.3924 | 95.7487 | 95.7539 | 11.7 | 77.9104 |
| 0.0087 | 50.0 | 134000 | 0.3391 | 95.8866 | 93.3924 | 95.7487 | 95.7539 | 11.7 | 77.9104 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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}
}
} | 855 | null | Access to model dalpaka/easy is restricted and you are not in the authorized list. Visit https://huggingface.co/dalpaka/easy to ask for access. |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 75 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="YoavWigelman/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| token-classification | {
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"BertForTokenClassification"
],
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} | 229 | null | ---
license: mit
datasets:
- squad_v2
- squad
language:
- en
library_name: transformers
pipeline_tag: question-answering
tags:
- deberta
- deberta-v3
- question-answering
- squad
- squad_v2
model-index:
- name: sjrhuschlee/deberta-v3-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exact_match
value: 85.648
name: Exact Match
- type: f1
value: 88.728
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 87.862
name: Exact Match
- type: f1
value: 93.924
name: F1
---
# deberta-v3-base for Extractive QA
This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering.
## Overview
**Language model:** deberta-v3-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Infrastructure**: 1x NVIDIA 3070
## Model Usage
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/deberta-v3-base-squad2"
# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Metrics
```bash
# Squad v2
{
"eval_HasAns_exact": 82.72604588394061,
"eval_HasAns_f1": 88.89430905100325,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 88.56181665264928,
"eval_NoAns_f1": 88.56181665264928,
"eval_NoAns_total": 5945,
"eval_best_exact": 85.64810915522614,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 88.72782481717712,
"eval_best_f1_thresh": 0.0,
"eval_exact": 85.64810915522614,
"eval_f1": 88.72782481717726,
"eval_runtime": 219.6226,
"eval_samples": 11951,
"eval_samples_per_second": 54.416,
"eval_steps_per_second": 2.268,
"eval_total": 11873
}
# Squad
{
"eval_exact_match": 87.86187322611164,
"eval_f1": 93.92373735474943,
"eval_runtime": 195.2115,
"eval_samples": 10618,
"eval_samples_per_second": 54.392,
"eval_steps_per_second": 2.269
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 0.1
- num_epochs: 4.0
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3 |
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 25 | null | Access to model xyz-nlp/XuanYuan2.0 is restricted and you are not in the authorized list. Visit https://huggingface.co/xyz-nlp/XuanYuan2.0 to ask for access. |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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}
}
} | 52 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sec-t5-base
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. -->
# sec-t5-base
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0365
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4845 | 1.0 | 24 | 3.1348 |
| 3.3224 | 2.0 | 48 | 3.0846 |
| 3.2575 | 3.0 | 72 | 3.0575 |
| 3.2128 | 4.0 | 96 | 3.0406 |
| 3.2031 | 5.0 | 120 | 3.0365 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
],
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}
} | 21 | null | ---
tags:
- conversational
---
# Rocket DialoGPT Model |
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
}
} | 26 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-token-class
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. -->
# roberta-token-class
This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2427
- Precision: 0.8626
- Recall: 0.7448
- F1: 0.7875
- Accuracy: 0.9168
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2378 | 1.0 | 1796 | 0.2433 | 0.8789 | 0.7063 | 0.7578 | 0.9121 |
| 0.1824 | 2.0 | 3592 | 0.2427 | 0.8626 | 0.7448 | 0.7875 | 0.9168 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CL/safe-math-bot | []
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} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Xmm/autotrain-data-pegasus-x-large
co2_eq_emissions:
emissions: 11.841975053683889
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 58281133234
- CO2 Emissions (in grams): 11.8420
## Validation Metrics
- Loss: 0.712
- Rouge1: 51.830
- Rouge2: 37.988
- RougeL: 44.541
- RougeLsum: 45.992
- Gen Len: 128.541
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Xmm/autotrain-pegasus-x-large-58281133234
``` |
CM-CA/DialoGPT-small-cartman | []
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} | 0 | null | Access to model galdleduc/Assignment3Lab5MonitoringPartB is restricted and you are not in the authorized list. Visit https://huggingface.co/galdleduc/Assignment3Lab5MonitoringPartB to ask for access. |
Calamarii/calamari | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: PranjalSarin/distilbert-base-uncased-finetuned-squad
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. -->
# PranjalSarin/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9615
- Train End Logits Accuracy: 0.7330
- Train Start Logits Accuracy: 0.6931
- Validation Loss: 1.1213
- Validation End Logits Accuracy: 0.6987
- Validation Start Logits Accuracy: 0.6676
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, '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 |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.4893 | 0.6116 | 0.5721 | 1.1497 | 0.6873 | 0.6525 | 0 |
| 0.9615 | 0.7330 | 0.6931 | 1.1213 | 0.6987 | 0.6676 | 1 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| summarization | {
"architectures": [
"MT5ForConditionalGeneration"
],
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},
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} | 16 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Xmm/autotrain-data-pegasus-x-base-finetuned-multi-news
co2_eq_emissions:
emissions: 7.52869711790795
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 58291133236
- CO2 Emissions (in grams): 7.5287
## Validation Metrics
- Loss: 0.794
- Rouge1: 56.481
- Rouge2: 40.769
- RougeL: 47.369
- RougeLsum: 49.028
- Gen Len: 130.354
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Xmm/autotrain-pegasus-x-base-finetuned-multi-news-58291133236
``` |
Canyonevo/DialoGPT-medium-KingHenry | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert_imdb_genre_classifier
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_imdb_genre_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0196
- Precision: 0.4254
- Recall: 0.4432
- F1 Score: 0.4191
- Jaccard Score: 0.2966
- Average Precision Score: 0.4831
- Percentage Examples At Least 1 True: 0.8845
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Score | Jaccard Score | Average Precision Score | Percentage Examples At Least 1 True |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:-------------:|:-----------------------:|:-----------------------------------:|
| 0.0231 | 1.0 | 1500 | 0.0214 | 0.3601 | 0.4090 | 0.3601 | 0.2523 | 0.4326 | 0.8638 |
| 0.0196 | 2.0 | 3000 | 0.0198 | 0.4174 | 0.4367 | 0.4064 | 0.2864 | 0.4743 | 0.8842 |
| 0.0172 | 3.0 | 4500 | 0.0196 | 0.4216 | 0.4418 | 0.4155 | 0.2939 | 0.4822 | 0.887 |
| 0.016 | 4.0 | 6000 | 0.0196 | 0.4254 | 0.4432 | 0.4191 | 0.2966 | 0.4831 | 0.8845 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 238 | null | ---
license: cc-by-nc-4.0
language:
- zh
---
CHaMA is a LLM sized 1B which is built for ALL NLP TASKS.A LLaMA-like Model which can be used in business is HERE by Change ALMOST EVETHING and Re-PRETRAINED FOR ZORE to ONE. |
Carlork314/Carlos | []
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} | 0 | null | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Blue Archive Yuuka VITS SVC 4.0 Voice Model
<!-- Provide a quick summary of what the model is/does. -->
Yuuka Hayase from Blue Archive voice model for SO VITS SVC 4.0
|
Cedille/fr-boris | [
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"arxiv:2202.03371",
"transformers",
"causal-lm",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPTJForCausalLM"
],
"model_type": "gptj",
"task_specific_params": {
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},
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} | 401 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.81 +/- 40.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-large-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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}
} | 3 | null | ---
language:
- en
tags:
- feature-extraction
- pubmed
- sentence-similarity
datasets:
- biu-nlp/abstract-sim-pubmed
---
A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Pubmed sentences. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def load_finetuned_model():
sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query-pubmed")
tokenizer = AutoTokenizer.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
return tokenizer, query_encoder, sentence_encoder
def encode_batch(model, tokenizer, sentences, device):
input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
add_special_tokens=True).to(device)
features = model(**input_ids)[0]
features = torch.sum(features[:,1:,:] * input_ids["attention_mask"][:,1:].unsqueeze(-1), dim=1) / torch.clamp(torch.sum(input_ids["attention_mask"][:,1:], dim=1, keepdims=True), min=1e-9)
return features
``` |
dccuchile/albert-large-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 5 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - valerizab/pokemon
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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"max_length": null
},
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}
} | 32 | null | ---
language:
- en
tags:
- pubmed
- feature-extraction
- sentence-similarity
datasets:
- biu-nlp/abstract-sim-pubmed
---
A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Pubmed sentences. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def load_finetuned_model():
sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query-pubmed")
tokenizer = AutoTokenizer.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
return tokenizer, query_encoder, sentence_encoder
def encode_batch(model, tokenizer, sentences, device):
input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
add_special_tokens=True).to(device)
features = model(**input_ids)[0]
features = torch.sum(features[:,1:,:] * input_ids["attention_mask"][:,1:].unsqueeze(-1), dim=1) / torch.clamp(torch.sum(input_ids["attention_mask"][:,1:], dim=1, keepdims=True), min=1e-9)
return features
``` |
dccuchile/albert-xlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 24 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ru_propaganda_opposition_model_distilbert-base-multilingual-cased
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. -->
# ru_propaganda_opposition_model_distilbert-base-multilingual-cased
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0003
- Validation Loss: 0.2582
- Train Accuracy: 0.9551
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7695, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2994 | 0.2306 | 0.9091 | 0 |
| 0.1127 | 0.1394 | 0.9540 | 1 |
| 0.0485 | 0.1417 | 0.9485 | 2 |
| 0.0256 | 0.1394 | 0.9562 | 3 |
| 0.0158 | 0.1835 | 0.9617 | 4 |
| 0.0106 | 0.1984 | 0.9617 | 5 |
| 0.0082 | 0.2812 | 0.9376 | 6 |
| 0.0030 | 0.2452 | 0.9562 | 7 |
| 0.0033 | 0.2022 | 0.9595 | 8 |
| 0.0052 | 0.2328 | 0.9529 | 9 |
| 0.0022 | 0.2302 | 0.9573 | 10 |
| 0.0019 | 0.2552 | 0.9529 | 11 |
| 0.0019 | 0.2461 | 0.9584 | 12 |
| 0.0006 | 0.2569 | 0.9551 | 13 |
| 0.0003 | 0.2582 | 0.9551 | 14 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
}
} | 393 | null | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
}
} | 25 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 430.53 +/- 21.71
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 294.78 +/- 16.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 28 | null | ---
datasets:
- OpenAssistant/oasst1
language:
- ru
- en
metrics:
- accuracy
- bertscore
library_name: adapter-transformers
--- |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-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: 2.4253
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5416 | 1.0 | 157 | 2.4086 |
| 2.4908 | 2.0 | 314 | 2.3721 |
| 2.4523 | 3.0 | 471 | 2.3873 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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} | 5 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 13.00 +/- 14.51
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-1 | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
} | 7 | null | ---
license: creativeml-openrail-m
language:
- zh
tags:
- 梵蒂岡驅魔士線上看
- 梵蒂岡驅魔士線上看完整版
---
# 《梵蒂岡驅魔士》免費線上看~完整版(2023小鴨影音)
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對於這位舉世聞名且經驗豐富的驅魔師，導演阿弗瑞補充道：「阿摩特神父不只是一位虔誠的教徒，更是一位博學多聞且邏輯很好的心理學家。他認為98%的案例是現代醫學可以解決的，但是剩下2%無法解釋的懸案，才是我們電影中想要探討的。」與魔鬼交手千百回合的親身經歷，歷史上最駭人聽聞的附身案例，通通都被阿摩特神父詳細的紀載下來。
片長：103分 上映日期：2023/04/28
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至於《梵蒂岡驅魔士》電影版會不會有中文字幕？目前看了一下 atmovies.org 所有影片都會包含中文字幕，所以不用擔心會缺少繁體中文配音。
想要在第一時間免費看《梵蒂岡驅魔士》電影，建議先準備好爆米花和飲料，畢竟這次電影片長會長達5小時14分鐘。
台灣除了能透過 atmovies.org 觀賞《梵蒂岡驅魔士》以外，也可以選擇 Catchplay 或有線電視，如台灣大寬頻、凱擘大寬頻、TBC、大新店 / 屏南數位有線電視、台灣數位光訊科技集團、 Home+中嘉同樣都可以直接訂閱觀賞，不過這次會教大家用 atmovies.org 免費看梵蒂岡驅魔士方法。
用電腦和電視上也可以觀賞 《梵蒂岡驅魔士》
在 iPhone 和 Android 操作完成後，就可以直接透過電腦登入 atmovies.org 官網，同樣是可以直接透過電腦大螢幕觀賞《梵蒂岡驅魔士》電影。
如果要在電視上觀看《梵蒂岡驅魔士》電影，只能透過 iPhone / Android 投影、鏡像、AirPlay 或 Chromecast 方式。
關鍵字Google：
梵蒂岡驅魔士線上看
梵蒂岡驅魔士線上看完整版
梵蒂岡驅魔士線上看小鴨
梵蒂岡驅魔士線上看完整版小鴨
梵蒂岡驅魔士線上看小鴨 台灣
梵蒂岡驅魔士台灣
梵蒂岡驅魔士台灣上映
梵蒂岡驅魔士台灣小鴨
梵蒂岡驅魔士台灣下載
梵蒂岡驅魔士小鴨 2023 |
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
license: cc-by-4.0
language:
- en
library_name: nemo
pipeline_tag: token-classification
tags:
- G2P
- Grapheme-to-Phoneme
---
# English G2P token classification model
This is a non-autoregressive model for English grapheme-to-phoneme (G2P) conversion based on BERT architecture. It predicts phonemes in CMU format.
Initial data was built using CMUdict v0.07
## Intended uses & limitations
The input is expected to contain english words consisting of latin letters and apostrophe, all letters separated by space.
### How to use
Install NeMo.
Download en_g2p.nemo (this model)
```bash
git lfs install
git clone https://huggingface.co/bene-ges/en_g2p_cmu_bert_large
```
Run
```bash
python ${NEMO_ROOT}/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \
pretrained_model=en_g2p_cmu_bert_large/en_g2p.nemo \
inference.from_file=input.txt \
inference.out_file=output.txt \
model.max_sequence_len=64 \
inference.batch_size=128 \
lang=en
```
Example of input file:
```
g e f f e r t
p r o s c r i b e d
p r o m i n e n t l y
j o c e l y n
m a r c e c a ' s
s t a n k o w s k i
m u f f l e
```
Example of output file:
```
G EH1 F ER0 T g e f f e r t G EH1 <DELETE> F <DELETE> ER0 T G EH1 <DELETE> F <DELETE> ER0 T PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
P R OW0 S K R AY1 B D p r o s c r i b e d P R OW0 S K R AY1 B <DELETE> D P R OW0 S K R AY1 B <DELETE> D PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
P R AA1 M AH0 N AH0 N T L IY0 p r o m i n e n t l y P R AA1 M AH0 N AH0 N T L IY0 P R AA1 M AH0 N AH0 N T L IY0 PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
JH AO1 S L IH0 N j o c e l y n JH AO1 S <DELETE> L IH0 N JH AO1 S <DELETE> L IH0 N PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
M AA0 R S EH1 K AH0 Z m a r c e c a ' s M AA0 R S EH1 K AH0 <DELETE> Z M AA0 R S EH1 K AH0 <DELETE> Z PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
S T AH0 NG K AO1 F S K IY0 s t a n k o w s k i S T AH0 NG K AO1 F S K IY0 S T AH0 NG K AO1 F S K IY0 PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
M AH1 F AH0L m u f f l e M AH1 <DELETE> F AH0_L <DELETE> M AH1 <DELETE> F AH0_L <DELETE> PLAIN PLAIN PLAIN PLAIN PLAIN PLAIN
```
Note that the correct output tags are in the **third** column, input is in the second column.
Tags correspond to input letters in a one-to-one fashion. If you remove `<DELETE>` tag, and replace `_` with space, you should get CMU-like transcription.
### How to use for TTS
See this [script](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/tts/tts_en_infer_from_cmu_phonemes.py) to run TTS directly from CMU phonemes.
|
Chaddmckay/Cdm | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bartpho-word-base
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. -->
# bartpho-word-base
This model is a fine-tuned version of [vinai/bartpho-word-base](https://huggingface.co/vinai/bartpho-word-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1067
- Rouge1: 49.261
- Rouge2: 22.7386
- Rougel: 33.4517
- Rougelsum: 35.6277
- Gen Len: 19.8061
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.7155 | 1.0 | 309 | 2.1805 | 48.1588 | 21.5414 | 32.4169 | 34.6188 | 19.7453 |
| 2.2951 | 2.0 | 619 | 2.1331 | 48.5623 | 21.8348 | 32.7922 | 34.9789 | 19.7525 |
| 2.1676 | 3.0 | 929 | 2.1193 | 49.2433 | 22.8931 | 33.4674 | 35.738 | 19.7881 |
| 2.057 | 4.0 | 1239 | 2.1210 | 49.2825 | 22.7084 | 33.4667 | 35.5888 | 19.7759 |
| 2.0153 | 4.99 | 1545 | 2.1067 | 49.261 | 22.7386 | 33.4517 | 35.6277 | 19.8061 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.12.0+cu102
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chaima/TunBerto | []
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} | 0 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - valerizab/pokemon2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
Chalponkey/DialoGPT-small-Barry | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 11 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.34 +/- 5.46
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Bhanu9Prakash/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Chandanbhat/distilbert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.36 +/- 0.36
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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