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Nadav/camembert-base-finetuned-on-runaways-fr | a110f5b1e593522b3d8ed450c31926924d7a0459 | 2022-06-19T14:24:02.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Nadav | null | Nadav/camembert-base-finetuned-on-runaways-fr | 3 | null | transformers | 22,600 | Entry not found |
Nadav/camembert-base-squad-finetuned-on-runaways-fr | d16dc75f9a7a7209b0df14b42026f9237cfd1256 | 2022-06-19T16:15:05.000Z | [
"pytorch",
"camembert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Nadav | null | Nadav/camembert-base-squad-finetuned-on-runaways-fr | 3 | null | transformers | 22,601 | Entry not found |
Nonnyss/music-wav2vec2-th-finetune-mark2 | a7f302fe6d705dabc5bfc46f981afd5de32a005b | 2022-06-16T09:50:21.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Nonnyss | null | Nonnyss/music-wav2vec2-th-finetune-mark2 | 3 | null | transformers | 22,602 | Entry not found |
S2312dal/M1_MLM | c737f41e3252e905e0df5c3688b5a986eec3820c | 2022-06-16T15:54:27.000Z | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | S2312dal | null | S2312dal/M1_MLM | 3 | null | transformers | 22,603 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M1_MLM
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. -->
# M1_MLM
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.2418 | 1.0 | 25 | 2.4870 |
| 2.4653 | 2.0 | 50 | 2.3762 |
| 2.2127 | 3.0 | 75 | 2.3000 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
anantoj/T5-summarizer-simple-wiki-v2 | 2d34e7bc1391f14a5c694fbd391c2abb5aeeb2c2 | 2022-06-16T16:44:54.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | anantoj | null | anantoj/T5-summarizer-simple-wiki-v2 | 3 | null | transformers | 22,604 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: T5-summarizer-simple-wiki-v2
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. -->
# T5-summarizer-simple-wiki-v2
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: 2.0866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2575 | 1.0 | 14719 | 2.1173 |
| 2.2663 | 2.0 | 29438 | 2.0926 |
| 2.2092 | 3.0 | 44157 | 2.0866 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
loubnabnl/codeparrot-small-filtered-data | 49b0db5f966ba7607ef5e6ce4a2d950bd3d5ca67 | 2022-06-17T12:32:08.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | loubnabnl | null | loubnabnl/codeparrot-small-filtered-data | 3 | null | transformers | 22,605 | ---
license: apache-2.0
---
|
eslamxm/MBart-finetuned-ur-xlsum | 651940a40484a975fb47985e0414b6a622df437d | 2022-06-17T14:59:58.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:xlsum",
"transformers",
"summarization",
"ur",
"seq2seq",
"Abstractive Summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/MBart-finetuned-ur-xlsum | 3 | null | transformers | 22,606 | ---
tags:
- summarization
- ur
- seq2seq
- mbart
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: MBart-finetuned-ur-xlsum
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. -->
# MBart-finetuned-ur-xlsum
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2663
- Rouge-1: 40.6
- Rouge-2: 18.9
- Rouge-l: 34.39
- Gen Len: 37.88
- Bertscore: 77.06
## 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: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP | d65bdc87bda4e4a3cef4cbb3515c67436d5673a0 | 2022-06-17T01:52:49.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:swiss_judgment_prediction",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | tuni | null | tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP | 3 | null | transformers | 22,607 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- swiss_judgment_prediction
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-finetuned-mnli-SJP
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: swiss_judgment_prediction
type: swiss_judgment_prediction
args: all_languages
metrics:
- name: Accuracy
type: accuracy
value: 0.7957142857142857
---
<!-- 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-large-xnli-finetuned-mnli-SJP
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3456
- Accuracy: 0.7957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 1.8460 | 0.7956 |
| No log | 2.0 | 10 | 1.3456 | 0.7957 |
| No log | 3.0 | 15 | 1.2799 | 0.7957 |
| No log | 4.0 | 20 | 1.2866 | 0.7957 |
| No log | 5.0 | 25 | 1.3162 | 0.7956 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
gciaffoni/modelLM | 313390a009926498686f2b97416ef03bdbcd8614 | 2022-07-20T15:40:02.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | gciaffoni | null | gciaffoni/modelLM | 3 | null | transformers | 22,608 | R4 checkpoint-30000 LM parlamento europeo
|
hakurei/litv2-6B-rev1 | 5ef6fd5bb9844d66ca2327f82175cd3b68adf9a4 | 2022-06-17T04:22:48.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | hakurei | null | hakurei/litv2-6B-rev1 | 3 | null | transformers | 22,609 | https://wandb.ai/haruu/mesh-transformer-jax/runs/68jerq7d?workspace=user-haruu |
erickfm/rosy-sweep-3 | 0d9db01c9c71dd145e76a5d6dd1b7e6ad22a38a3 | 2022-06-17T07:54:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/rosy-sweep-3 | 3 | null | transformers | 22,610 | Entry not found |
powerwarez/kindword-model | 02b37fd36aae65c2e4a9daf639a5652bdf16e56a | 2022-06-17T11:11:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | powerwarez | null | powerwarez/kindword-model | 3 | null | transformers | 22,611 | ---
tags:
- generated_from_trainer
model-index:
- name: kindword-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. -->
# kindword-model
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
EddieChen372/gpt2-jest | 137722ae59766b5cc2eb5d8522091fe967a69189 | 2022-06-23T08:04:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | EddieChen372 | null | EddieChen372/gpt2-jest | 3 | null | transformers | 22,612 | Entry not found |
Nadav/robbert-base-finetuned-on-runaways-nl | 0810b7e77ee8294bab8c15952ccc4481379d944f | 2022-06-18T09:07:21.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Nadav | null | Nadav/robbert-base-finetuned-on-runaways-nl | 3 | null | transformers | 22,613 | Entry not found |
sgraf202/finetuning-sentiment-model-3000-samples | 19a2289e851f3518cd71e3c150638d87783b0392 | 2022-07-11T10:57:24.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | sgraf202 | null | sgraf202/finetuning-sentiment-model-3000-samples | 3 | null | transformers | 22,614 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7404
- Accuracy: 0.4688
- F1: 0.5526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dominguesm/stt_pt_quartznet15x5_ctc_small | 14fecbfd291ade80a3624d5c2399a30be2d6fe49 | 2022-06-26T01:05:06.000Z | [
"nemo",
"pt",
"dataset:mozilla-foundation/common_voice_9_0",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"QuartzNet",
"Transformer",
"NeMo",
"pytorch",
"license:cc-by-4.0",
"model-index"
] | automatic-speech-recognition | false | dominguesm | null | dominguesm/stt_pt_quartznet15x5_ctc_small | 3 | 2 | nemo | 22,615 | ---
language:
- pt
license: cc-by-4.0
library_name: nemo
datasets:
- mozilla-foundation/common_voice_9_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- QuartzNet
- Transformer
- NeMo
- pytorch
model-index:
- name: stt_pt_quartznet15x5_ctc_small
results:
- task:
type: automatic-speech-recognition
dataset:
type: common_voice
name: Common Voice Portuguese
config: clean
split: test
args:
language: pt
metrics:
- type: wer
value: 49.17
name: Test WER
- type: cer
value: 18.59
name: Test CER
---
## Model Overview
This model transcribes speech in lower case Portuguese alphabet along with spaces. It is a "small" versions of QuartzNet-CTC model.
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("dominguesm/stt_pt_quartznet15x5_ctc_small")
```
### Transcribing using Python
First, let's get a sample
```
wget https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small/raw/main/audios/common_voice_pt_25555332.mp3
```
Then simply do:
```
asr_model.transcribe(['common_voice_pt_25555332.mp3'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="dominguesm/stt_pt_quartznet15x5_ctc_small" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
This model are based on the QuartzNet architecture, which is a variant of Jasper that uses 1D time-channel separable convolutional layers in its convolutional residual blocks and are therefore smaller than Jasper models.
QuartzNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.
## Training
All training scripts will be available at: [DominguesM/stt_pt_quartznet15x5_ctc_small](https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small)
### Datasets
The model was trained with a part of the Common Voices 9.0 dataset in Portuguese, totaling 26 hours of audio.
* Mozilla Common Voice (v9.0)
## Performance
| Metric | Score |
| ------- | ----- |
| WER | 49% |
| CER | 18% |
The metrics were obtained using the following code:
**Attention**: The steps below must be performed after downloading the dataset (Mozilla Commom Voices 9.0 PT) and following the steps of pre-processing the audio data and `manifest` files contained in the file [`notebooks/Finetuning CTC model Portuguese.ipynb`](https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small)
```bash
$ wget -P scripts/ "https://raw.githubusercontent.com/NVIDIA/NeMo/v1.9.0/examples/asr/speech_to_text_eval.py"
$ wget -P scripts/ "https://raw.githubusercontent.com/NVIDIA/NeMo/v1.9.0/examples/asr/transcribe_speech.py"
$ python scripts/speech_to_text_eval.py \
pretrained_name="dominguesm/stt_pt_quartznet15x5_ctc_small" \
dataset_manifest="manifests/pt/commonvoice_test_manifest_processed.json" \
output_filename="./evaluation_transcripts.json" \
batch_size=32 \
amp=true \
use_cer=false
```
## Limitations
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## Citation
If you use our work, please cite:
```cite
@misc{domingues2022quartznet15x15-small-portuguese,
title={Fine-tuned {Quartznet}-15x5 CTC small model for speech recognition in {P}ortuguese},
author={Domingues, Maicon},
howpublished={\url{https://huggingface.co/dominguesm/stt_pt_quartznet15x5_ctc_small}},
year={2022}
}
```
## References
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
huggingtweets/g2esports | 107c777b4b085294c497eb44704f012c5bc34513 | 2022-06-19T18:55:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/g2esports | 3 | null | transformers | 22,616 | ---
language: en
thumbnail: http://www.huggingtweets.com/g2esports/1655664936018/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1531198610129428480/GoplyEsx_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">G2 Esports</div>
<div style="text-align: center; font-size: 14px;">@g2esports</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from G2 Esports.
| Data | G2 Esports |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 342 |
| Short tweets | 938 |
| Tweets kept | 1970 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1h6b63sz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @g2esports's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/724imy81) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/724imy81/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/g2esports')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
joshanashakya/codebert_sourcecode_nmt_pn2ja_100E_2e-05LR_16B_12E_12D | 53e8ce99cf2be7383e3971e4c8acb27a9cce2df3 | 2022-06-20T03:42:08.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_pn2ja_100E_2e-05LR_16B_12E_12D | 3 | null | transformers | 22,617 | Entry not found |
anonsubms/lm_giga | a52bfc4563b0720fdb568ffdf0fd33a48fc6440a | 2022-06-21T04:40:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | anonsubms | null | anonsubms/lm_giga | 3 | null | transformers | 22,618 | Entry not found |
roshnir/xlmr-finetuned-mlqa-dev-cross-de-hi | 48a6d1164fabbceb3dacbdf25e4d783454162680 | 2022-06-21T18:52:49.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-cross-de-hi | 3 | null | transformers | 22,619 | Entry not found |
lmqg/bart-base-squadshifts-reddit | e5cdd82a07cdb96ae1014033214e6349d7f1033c | 2022-06-22T10:48:50.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/bart-base-squadshifts-reddit | 3 | null | transformers | 22,620 | Entry not found |
transZ/M2M_Vi_Ba | b5d39b2b5ccbfc35084db2cb692b7f0a5af959f6 | 2022-06-23T11:01:27.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"vi",
"ba",
"dataset:custom dataset",
"transformers",
"translation",
"autotrain_compatible"
] | translation | false | transZ | null | transZ/M2M_Vi_Ba | 3 | null | transformers | 22,621 | ---
language:
- vi
- ba
tags:
- translation
datasets:
- custom dataset
metrics:
- bleu
- sacrebleu
---
# How to run the model
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
model = M2M100ForConditionalGeneration.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer = M2M100Tokenizer.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer.src_lang = "vi"
vi_text = "Hôm nay ba đi chợ."
encoded_vi = tokenizer(vi_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_vi, forced_bos_token_id=tokenizer.get_lang_id("ba"))
translate = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(translate)
``` |
munggok/infoxlm-large-squad | 46a7dd085e6d5b74918c2064e2b0eb97906db89d | 2022-06-22T23:44:33.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | munggok | null | munggok/infoxlm-large-squad | 3 | null | transformers | 22,622 | Entry not found |
aico/TrOCR-MNIST | 2e3f28ee86838a11811d0c17e4a85f0d981c95b7 | 2022-06-23T10:38:57.000Z | [
"pytorch",
"vision-encoder-decoder",
"transformers"
] | null | false | aico | null | aico/TrOCR-MNIST | 3 | null | transformers | 22,623 | Fine Tune MNIST dataset on the ViT TrOCR model
accuracy = 0.99525
ref:
http://yann.lecun.com/exdb/mnist/
https://github.com/microsoft/unilm/tree/master/trocr |
Rahulrr/language_model_en_he | 82e265712f0f6453f5e684815b657b4afb35358f | 2022-06-24T05:31:17.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"he",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Rahulrr | null | Rahulrr/language_model_en_he | 3 | null | transformers | 22,624 | ---
language:
- en
- he
tags:
- translation
license: apache-2.0
---
### en-he
* source group: English
* target group: Hebrew
* OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md)
* model: transformer-align
* source language(s): eng
* target language(s): heb
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus+bt-2021-04-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus+bt-2021-04-13.zip)
* test set translations: [opus+bt-2021-04-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus+bt-2021-04-13.test.txt)
* test set scores: [opus+bt-2021-04-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus+bt-2021-04-13.eval.txt)
## Benchmarks
| testset | BLEU | chr-F | #sent | #words | BP |
|---------|-------|-------|-------|--------|----|
| Tatoeba-test.eng-heb | 37.8 | 0.601 | 10000 | 60359 | 1.000 |
### System Info:
- hf_name: en-he
- source_languages: eng
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'he']
- src_constituents: ('English', {'eng'})
- tgt_constituents: ('Hebrew', {'heb'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: eng-heb
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus+bt-2021-04-13.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus+bt-2021-04-13.test.txt
- src_alpha3: eng
- tgt_alpha3: heb
- chrF2_score: 0.601
- bleu: 37.8
- src_name: English
- tgt_name: Hebrew
- train_date: 2021-04-13 00:00:00
- src_alpha2: en
- tgt_alpha2: he
- prefer_old: False
- short_pair: en-he
- helsinki_git_sha: c4e978d8de47875b482653b423dcfe968979d7d5
- transformers_git_sha: 56b83cf049823ed074a655eceb28f31e2077c6eb
- port_machine: LAPIN4GLQ2G3
- port_time: 2022-06-22-19:47 |
mohsenfayyaz/bert-base-parsbert-uncased_parsquad | b8753838f4efafb90873ff5e524634ff06f48801 | 2022-06-24T09:50:19.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mohsenfayyaz | null | mohsenfayyaz/bert-base-parsbert-uncased_parsquad | 3 | null | transformers | 22,625 | Entry not found |
Splend1dchan/t5lephone-small-textsquad | 8d328502424903feb4aef85e0157ef44dcf0a934 | 2022-06-24T09:47:41.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Splend1dchan | null | Splend1dchan/t5lephone-small-textsquad | 3 | null | transformers | 22,626 | Entry not found |
Servarr/bert-finetuned-radarr | 86c3322b93aea497da160969fef732b08457ef4e | 2022-06-24T16:40:53.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:movie_releases",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | Servarr | null | Servarr/bert-finetuned-radarr | 3 | null | transformers | 22,627 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- movie_releases
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-radarr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: movie_releases
type: movie_releases
args: default
metrics:
- name: Precision
type: precision
value: 0.9555421444377389
- name: Recall
type: recall
value: 0.9638798701298701
- name: F1
type: f1
value: 0.9596928982725529
- name: Accuracy
type: accuracy
value: 0.9817602584524263
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-radarr
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the movie_releases dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0731
- Precision: 0.9555
- Recall: 0.9639
- F1: 0.9597
- Accuracy: 0.9818
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0431 | 1.0 | 1191 | 0.1403 | 0.9436 | 0.9574 | 0.9504 | 0.9626 |
| 0.0236 | 2.0 | 2382 | 0.0881 | 0.9485 | 0.9560 | 0.9522 | 0.9694 |
| 0.0138 | 3.0 | 3573 | 0.0731 | 0.9555 | 0.9639 | 0.9597 | 0.9818 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mohsenfayyaz/albert-fa-base-v2_pquad_and_persian_qa | 72fbfa8544039979e34e6f930965b95c59e87446 | 2022-06-24T12:19:42.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mohsenfayyaz | null | mohsenfayyaz/albert-fa-base-v2_pquad_and_persian_qa | 3 | null | transformers | 22,628 | Entry not found |
mohsenfayyaz/bert-base-parsbert-uncased_pquad_lr1e-5 | 8aa35d79be0344a5ae407047ff55475bfd9aef93 | 2022-06-24T13:06:50.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mohsenfayyaz | null | mohsenfayyaz/bert-base-parsbert-uncased_pquad_lr1e-5 | 3 | null | transformers | 22,629 | Entry not found |
voidful/phoneme-longt5-global | ec8941a24d8bf89f826ce397b537ebe13bd61411 | 2022-06-25T04:36:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | voidful | null | voidful/phoneme-longt5-global | 3 | null | transformers | 22,630 | Entry not found |
Chemsseddine/bert2gpt2_med_v2 | f78142c0c70fa982258ce6d1d503597d0624cd7d | 2022-06-30T19:53:14.000Z | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Chemsseddine | null | Chemsseddine/bert2gpt2_med_v2 | 3 | null | transformers | 22,631 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert2gpt2_med_v2
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. -->
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
# bert2gpt2_med_v2
This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0684
- Rouge1: 34.1248
- Rouge2: 17.7006
- Rougel: 33.4661
- Rougelsum: 33.4419
- Gen Len: 22.6429
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.9107 | 1.0 | 1000 | 2.0877 | 30.4547 | 14.4024 | 30.3642 | 30.3788 | 21.9714 |
| 1.8782 | 2.0 | 2000 | 1.8151 | 32.6607 | 16.8089 | 32.3844 | 32.4762 | 21.7714 |
| 1.291 | 3.0 | 3000 | 1.7523 | 33.6391 | 16.7866 | 32.4256 | 32.3306 | 22.7429 |
| 0.819 | 4.0 | 4000 | 1.7650 | 35.0633 | 19.1222 | 34.4902 | 34.6796 | 22.4714 |
| 0.4857 | 5.0 | 5000 | 1.8129 | 33.8763 | 16.9303 | 32.8845 | 32.9225 | 22.3857 |
| 0.3232 | 6.0 | 6000 | 1.9339 | 33.9272 | 17.1784 | 32.9301 | 33.0253 | 22.4286 |
| 0.2022 | 7.0 | 7000 | 1.9634 | 33.9869 | 16.4238 | 33.7336 | 33.65 | 22.6429 |
| 0.1452 | 8.0 | 8000 | 2.0090 | 33.8892 | 18.2723 | 33.7514 | 33.6531 | 22.5714 |
| 0.0845 | 9.0 | 9000 | 2.0337 | 33.9649 | 17.1339 | 33.5061 | 33.4157 | 22.7857 |
| 0.0531 | 10.0 | 10000 | 2.0684 | 34.1248 | 17.7006 | 33.4661 | 33.4419 | 22.6429 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mikesong724/deberta-wiki-2006 | 97086580b99643ca1400ade3e273dd48ad25af8b | 2022-06-25T17:11:39.000Z | [
"pytorch",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mikesong724 | null | mikesong724/deberta-wiki-2006 | 3 | null | transformers | 22,632 | DeBERTa trained from scratch
Source data: https://dumps.wikimedia.org/archive/2006/
Tools used: https://github.com/mikesong724/Point-in-Time-Language-Model
2006 wiki archive 2.7 GB trained 24 epochs = 65GB
GLUE benchmark
cola (3e): matthews corr: 0.2848
sst2 (3e): acc: 0.8876
mrpc (5e): F1: 0.8033, acc: 0.7108
stsb (3e): pearson: 0.7542, spearman: 0.7536
qqp (3e): acc: 0.8852, F1: 0.8461
mnli (3e): acc_mm: 0.7822
qnli (3e): acc: 0.8715
rte (3e): acc: 0.5235
wnli (5e): acc: 0.3099 |
haritzpuerto/xtremedistil-l6-h256-uncased-squad_1.1 | f893dedca53ad8ca62c1a3746e82f9dcecef7500 | 2022-06-25T19:13:22.000Z | [
"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad",
"transformers",
"QA",
"Question Answering",
"SQuAD",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | haritzpuerto | null | haritzpuerto/xtremedistil-l6-h256-uncased-squad_1.1 | 3 | null | transformers | 22,633 | ---
language:
- en
tags:
- QA
- Question Answering
- SQuAD
license: "mit"
datasets:
- squad
metrics:
- squad
model-index:
- name: xtremedistil-l6-h256-uncased
results:
- task:
type: question-answering # Required. Example: automatic-speech-recognition
name: Question Answering # Optional. Example: Speech Recognition
dataset:
type: squad # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: SQuAD # Required. A pretty name for the dataset. Example: Common Voice (French)
split: validation # Optional. Example: test
metrics:
- type: squad # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 62.66792809839168 # Required. Example: 20.90
name: SQuAD EM # Optional. Example: Test WER
config: exact_match # Optional. The name of the metric configuration used in `load_metric()`. Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
- type: squad # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 74.99490608582015 # Required. Example: 20.90
name: SQuAD F1 # Optional. Example: Test WER
config: F1
---
microsoft/xtremedistil-l6-h256-uncased fined-tuned on SQuAD (https://huggingface.co/datasets/squad)
Hyperparameters:
- epochs: 1
- lr: 1e-5
- train batch sie: 16
- optimizer: adamW
- lr_scheduler: linear
- num warming steps: 0
- max_length: 512
Results on the dev set:
- 'exact_match': 62.66792809839168
- 'f1': 74.99490608582015
|
ipvikas/vit-demo | fc1d9e153faafc87946908454afd4796cb9e0887 | 2022-06-26T02:00:52.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers"
] | image-classification | false | ipvikas | null | ipvikas/vit-demo | 3 | null | transformers | 22,634 | Entry not found |
hyan97/distilbert-base-uncased-finetuned-squad | d8f82ef19199e7e2119f387313630491257699f4 | 2022-06-26T05:55:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | hyan97 | null | hyan97/distilbert-base-uncased-finetuned-squad | 3 | null | transformers | 22,635 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3517
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2094 | 1.0 | 8235 | 1.2174 |
| 0.9515 | 2.0 | 16470 | 1.1923 |
| 0.7687 | 3.0 | 24705 | 1.3517 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
BitanBiswas/deepD | 6ad3656ba96c0224a8760ff1186c3b3d9dcc2a53 | 2022-06-26T12:12:50.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | BitanBiswas | null | BitanBiswas/deepD | 3 | null | transformers | 22,636 | Entry not found |
sudo-s/exper_batch_8_e4 | 5364fbcc570c2b5ee3cec3bfbab4ddd79709efa5 | 2022-06-26T15:33:41.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | sudo-s | null | sudo-s/exper_batch_8_e4 | 3 | null | transformers | 22,637 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_8_e4
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. -->
# exper_batch_8_e4
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 sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3353
- Accuracy: 0.9183
## 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: 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: 4
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2251 | 0.08 | 100 | 4.1508 | 0.1203 |
| 3.4942 | 0.16 | 200 | 3.5566 | 0.2082 |
| 3.2871 | 0.23 | 300 | 3.0942 | 0.3092 |
| 2.7273 | 0.31 | 400 | 2.8338 | 0.3308 |
| 2.4984 | 0.39 | 500 | 2.4860 | 0.4341 |
| 2.3423 | 0.47 | 600 | 2.2201 | 0.4796 |
| 1.8785 | 0.55 | 700 | 2.1890 | 0.4653 |
| 1.8012 | 0.63 | 800 | 1.9901 | 0.4865 |
| 1.7236 | 0.7 | 900 | 1.6821 | 0.5736 |
| 1.4949 | 0.78 | 1000 | 1.5422 | 0.6083 |
| 1.5573 | 0.86 | 1100 | 1.5436 | 0.6110 |
| 1.3241 | 0.94 | 1200 | 1.4077 | 0.6207 |
| 1.0773 | 1.02 | 1300 | 1.1417 | 0.6916 |
| 0.7935 | 1.1 | 1400 | 1.1194 | 0.6931 |
| 0.7677 | 1.17 | 1500 | 1.0727 | 0.7167 |
| 0.9468 | 1.25 | 1600 | 1.0707 | 0.7136 |
| 0.7563 | 1.33 | 1700 | 0.9427 | 0.7390 |
| 0.8471 | 1.41 | 1800 | 0.8906 | 0.7571 |
| 0.9998 | 1.49 | 1900 | 0.8098 | 0.7845 |
| 0.6039 | 1.57 | 2000 | 0.7244 | 0.8034 |
| 0.7052 | 1.64 | 2100 | 0.7881 | 0.7953 |
| 0.6753 | 1.72 | 2200 | 0.7458 | 0.7926 |
| 0.3758 | 1.8 | 2300 | 0.6987 | 0.8022 |
| 0.4985 | 1.88 | 2400 | 0.6286 | 0.8265 |
| 0.4122 | 1.96 | 2500 | 0.5949 | 0.8358 |
| 0.1286 | 2.04 | 2600 | 0.5691 | 0.8385 |
| 0.1989 | 2.11 | 2700 | 0.5535 | 0.8389 |
| 0.3304 | 2.19 | 2800 | 0.5261 | 0.8520 |
| 0.3415 | 2.27 | 2900 | 0.5504 | 0.8477 |
| 0.4066 | 2.35 | 3000 | 0.5418 | 0.8497 |
| 0.1208 | 2.43 | 3100 | 0.5156 | 0.8612 |
| 0.1668 | 2.51 | 3200 | 0.5655 | 0.8539 |
| 0.0727 | 2.58 | 3300 | 0.4971 | 0.8658 |
| 0.0929 | 2.66 | 3400 | 0.4962 | 0.8635 |
| 0.0678 | 2.74 | 3500 | 0.4903 | 0.8670 |
| 0.1212 | 2.82 | 3600 | 0.4357 | 0.8867 |
| 0.1579 | 2.9 | 3700 | 0.4642 | 0.8739 |
| 0.2625 | 2.98 | 3800 | 0.3994 | 0.8951 |
| 0.024 | 3.05 | 3900 | 0.3953 | 0.8971 |
| 0.0696 | 3.13 | 4000 | 0.3883 | 0.9056 |
| 0.0169 | 3.21 | 4100 | 0.3755 | 0.9086 |
| 0.023 | 3.29 | 4200 | 0.3685 | 0.9109 |
| 0.0337 | 3.37 | 4300 | 0.3623 | 0.9109 |
| 0.0123 | 3.45 | 4400 | 0.3647 | 0.9067 |
| 0.0159 | 3.52 | 4500 | 0.3630 | 0.9082 |
| 0.0154 | 3.6 | 4600 | 0.3522 | 0.9094 |
| 0.0112 | 3.68 | 4700 | 0.3439 | 0.9163 |
| 0.0219 | 3.76 | 4800 | 0.3404 | 0.9194 |
| 0.0183 | 3.84 | 4900 | 0.3371 | 0.9183 |
| 0.0103 | 3.92 | 5000 | 0.3362 | 0.9183 |
| 0.0357 | 3.99 | 5100 | 0.3353 | 0.9183 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
TheRensselaerIDEA/gpt2-large-covid-tweet-response | 78af37e631c7e6c1b7fa85df3fbb40ecfc975fb0 | 2022-06-27T07:26:54.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"arxiv:2204.04353",
"transformers",
"license:mit"
] | text-generation | false | TheRensselaerIDEA | null | TheRensselaerIDEA/gpt2-large-covid-tweet-response | 3 | null | transformers | 22,638 | ---
license: mit
---
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [COVID-19 public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (3.36 at 2 epochs) seen during training. See Training metrics for Tensorboard logs.
Also see: our [Vaccine public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-vaccine-tweet-response).
**Data input format:** <span style="color:red"><|message|></span>public health message<span style="color:red"><|author|></span>public health Twitter handle<span style="color:red"><|response|></span>
Example:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.trainer_utils import set_seed
import torch
tokenizer = AutoTokenizer.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response")
model = AutoModelForCausalLM.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
set_seed(33)
message = "Is your child worried about #COVID19? Learn the facts so you can answer your children’s questions."
author = "CDCgov"
num_responses = 2
author_token, message_token, response_token = tokenizer.additional_special_tokens
input_str = f"{message_token}{message}{author_token}{author}{response_token}"
inputs = tokenizer(input_str, return_tensors="pt").to(device)
responses_ids = model.generate(**inputs,
max_new_tokens=100,
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
top_p=0.95,
temperature=1.5,
num_beams=3,
early_stopping=True,
num_return_sequences=num_responses)
responses = [tokenizer.decode(r[inputs.input_ids.shape[-1]:], skip_special_tokens=True) for r in responses_ids]
for i, resp in enumerate(responses):
print(f"Response {i}: {resp}\n")
```
Output:
```
Response 0: @CDCgov I'm not worried. I don't know who needs to hear this, but I have a feeling I know who will be listening.
It is not the virus. It is the media. I know you and CDC have been lying for months now, but the media will keep pushing this lie.
Response 1: #WashYourHands to help #StopTheSpread of #COVID19 and other diseases. Learn more about hand washing: #HandWashing
```
|
Shanny/bert-finetuned-squad | c513aa02990cea43f67b1c1abf8afdf7c28d766d | 2022-06-28T10:07:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Shanny | null | Shanny/bert-finetuned-squad | 3 | null | transformers | 22,639 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vinayak361/token_final_tunned | 02316bc6f7fb360a7417461891dca8d32e57e672 | 2022-07-05T07:54:14.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | vinayak361 | null | vinayak361/token_final_tunned | 3 | null | transformers | 22,640 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_final_tunned
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. -->
# token_final_tunned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4670
- Precision: 0.8269
- Recall: 0.8442
- F1: 0.8355
- Accuracy: 0.8516
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 108 | 0.7286 | 0.6581 | 0.7117 | 0.6838 | 0.7272 |
| No log | 2.0 | 216 | 0.5497 | 0.7529 | 0.7823 | 0.7673 | 0.8053 |
| No log | 3.0 | 324 | 0.4884 | 0.7911 | 0.8145 | 0.8026 | 0.8277 |
| No log | 4.0 | 432 | 0.4723 | 0.8144 | 0.8278 | 0.8210 | 0.8408 |
| 0.6038 | 5.0 | 540 | 0.4597 | 0.8032 | 0.8315 | 0.8171 | 0.8428 |
| 0.6038 | 6.0 | 648 | 0.4583 | 0.8208 | 0.8322 | 0.8264 | 0.8480 |
| 0.6038 | 7.0 | 756 | 0.4641 | 0.8290 | 0.8442 | 0.8365 | 0.8520 |
| 0.6038 | 8.0 | 864 | 0.4670 | 0.8269 | 0.8442 | 0.8355 | 0.8516 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
egumasa/roberta-base-finetuned-academic | 7945b0138af5601f54453ce34139795307dad627 | 2022-06-28T05:06:29.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"dataset:elsevier-oa-cc-by",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | egumasa | null | egumasa/roberta-base-finetuned-academic | 3 | null | transformers | 22,641 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- elsevier-oa-cc-by
model-index:
- name: roberta-base-finetuned-academic
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-base-finetuned-academic
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1903 | 0.25 | 1025 | 2.0998 |
| 2.1752 | 0.5 | 2050 | 2.1186 |
| 2.1864 | 0.75 | 3075 | 2.1073 |
| 2.1874 | 1.0 | 4100 | 2.1177 |
| 2.1669 | 1.25 | 5125 | 2.1091 |
| 2.1859 | 1.5 | 6150 | 2.1212 |
| 2.1783 | 1.75 | 7175 | 2.1096 |
| 2.1734 | 2.0 | 8200 | 2.0998 |
| 2.1712 | 2.25 | 9225 | 2.0972 |
| 2.1812 | 2.5 | 10250 | 2.1051 |
| 2.1811 | 2.75 | 11275 | 2.1150 |
| 2.1826 | 3.0 | 12300 | 2.1097 |
| 2.172 | 3.25 | 13325 | 2.1115 |
| 2.1745 | 3.5 | 14350 | 2.1098 |
| 2.1758 | 3.75 | 15375 | 2.1101 |
| 2.1834 | 4.0 | 16400 | 2.1232 |
| 2.1836 | 4.25 | 17425 | 2.1052 |
| 2.1791 | 4.5 | 18450 | 2.1186 |
| 2.172 | 4.75 | 19475 | 2.1039 |
| 2.1797 | 5.0 | 20500 | 2.1015 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
chisun/mt5-small-finetuned-amazon-en-es-accelerate | 1a2a07fd001c3011eb859c77c7beafa65f8eb395 | 2022-06-27T07:52:26.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | chisun | null | chisun/mt5-small-finetuned-amazon-en-es-accelerate | 3 | null | transformers | 22,642 | Entry not found |
YuanWellspring/wav2vec2-nsc-final_1-google-colab | bcd4f0afd409c37eff52452d795afe1e180dc1c9 | 2022-06-27T09:21:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | YuanWellspring | null | YuanWellspring/wav2vec2-nsc-final_1-google-colab | 3 | null | transformers | 22,643 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-nsc-final_1-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-nsc-final_1-google-colab
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.10.3
|
PSW/xsum_samsum_threshold0.25_epoch3 | 5703b74bda2cb70fb3c80b20c404cf62f4bc7f69 | 2022-06-27T16:03:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/xsum_samsum_threshold0.25_epoch3 | 3 | null | transformers | 22,644 | Entry not found |
fvancesco/tmp_date | ca35035fa0be7785c87a4f1e52e980d7eff0dd0a | 2022-06-27T23:47:48.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | fvancesco | null | fvancesco/tmp_date | 3 | null | transformers | 22,645 | ---
license: mit
---
|
Adars/bert-base-cased-finetuned-squad | d4a1dd98ab1144cb926ca2f0c20814d06e690e1a | 2022-06-28T16:15:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Adars | null | Adars/bert-base-cased-finetuned-squad | 3 | null | transformers | 22,646 | Entry not found |
sumitrsch/muril_large_multiconer22_bn | ba06a04068ff99e43882024a2fbe060fa7056a69 | 2022-06-30T12:39:24.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | token-classification | false | sumitrsch | null | sumitrsch/muril_large_multiconer22_bn | 3 | 2 | transformers | 22,647 | ---
license: afl-3.0
---
|
Akihiro2/mt5-small-finetuned-amazon-en-es | 8d446a3ec88beb9eb0d07ff53dd7d40380f44b0a | 2022-07-14T03:35:54.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Akihiro2 | null | Akihiro2/mt5-small-finetuned-amazon-en-es | 3 | null | transformers | 22,648 | Entry not found |
roshnir/mBert-finetuned-mlqa-dev-samelen-en-hi | b4f4a1b981e647bb63b58b04cd0c30fb1546eecb | 2022-06-28T19:56:22.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/mBert-finetuned-mlqa-dev-samelen-en-hi | 3 | null | transformers | 22,649 | Entry not found |
RuiqianLi/wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun | 03d2c1b4ba74b4607b9c39e183c0b364f4ce96f3 | 2022-06-29T08:44:53.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:uob_singlish",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | RuiqianLi | null | RuiqianLi/wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun | 3 | null | transformers | 22,650 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-960h-lv60-self-4-gram_fine-tune_real_29_Jun
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the uob_singlish dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2895
- Wer: 0.4583
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1283 | 1.82 | 20 | 1.5236 | 0.5764 |
| 1.3015 | 3.64 | 40 | 1.2956 | 0.4931 |
| 0.9918 | 5.45 | 60 | 1.3087 | 0.5347 |
| 0.849 | 7.27 | 80 | 1.2914 | 0.5139 |
| 0.6191 | 9.09 | 100 | 1.2895 | 0.4583 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
coolzhao/xlm-roberta-base-finetuned-panx-de | 15e178f8aff0d73797e91da1c3c00f44f8d6e0a2 | 2022-06-29T07:14:20.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | coolzhao | null | coolzhao/xlm-roberta-base-finetuned-panx-de | 3 | null | transformers | 22,651 | ---
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
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8600306626540231
---
<!-- 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: 0.1356
- F1: 0.8600
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2525 | 1.0 | 525 | 0.1673 | 0.8294 |
| 0.1298 | 2.0 | 1050 | 0.1381 | 0.8510 |
| 0.0839 | 3.0 | 1575 | 0.1356 | 0.8600 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ss756/bert-base-cased-finetuned-squad | 4ee4277eb72c1a6ac4717e22011a68a81f53d6ca | 2022-07-04T10:21:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | ss756 | null | ss756/bert-base-cased-finetuned-squad | 3 | null | transformers | 22,652 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0081
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0071 | 1.0 | 22183 | 1.0081 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
yaakov/demo-transfer-learning | b37bacf13e08912efdc933d01ea70c9fce639e90 | 2022-06-29T13:59:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | yaakov | null | yaakov/demo-transfer-learning | 3 | null | transformers | 22,653 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: demo-transfer-learning
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8553921568627451
- name: F1
type: f1
value: 0.8991452991452993
---
<!-- 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. -->
# demo-transfer-learning
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: 0.6183
- Accuracy: 0.8554
- F1: 0.8991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.3771 | 0.8358 | 0.8784 |
| 0.5168 | 2.0 | 918 | 0.4530 | 0.8578 | 0.9033 |
| 0.3018 | 3.0 | 1377 | 0.6183 | 0.8554 | 0.8991 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
okite97/distilbert-base-uncased-finetuned-zindi_tweets | 2c5b7242b5317862dd95e0a020ff2abbb8338bf4 | 2022-06-29T15:36:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | okite97 | null | okite97/distilbert-base-uncased-finetuned-zindi_tweets | 3 | null | transformers | 22,654 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-zindi_tweets
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-zindi_tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3203
- Accuracy: 0.9168
- F1: 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4224 | 1.0 | 67 | 0.2924 | 0.8894 | 0.8893 |
| 0.2096 | 2.0 | 134 | 0.2632 | 0.9055 | 0.9055 |
| 0.1329 | 3.0 | 201 | 0.2744 | 0.9102 | 0.9101 |
| 0.1016 | 4.0 | 268 | 0.2868 | 0.9055 | 0.9054 |
| 0.0752 | 5.0 | 335 | 0.2896 | 0.9140 | 0.9140 |
| 0.0454 | 6.0 | 402 | 0.3077 | 0.9178 | 0.9178 |
| 0.0305 | 7.0 | 469 | 0.3185 | 0.9149 | 0.9149 |
| 0.0298 | 8.0 | 536 | 0.3203 | 0.9168 | 0.9168 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/BioRed-CD-Modified-PubMedBERT-512 | 6c163c2a9b1c6759027e4a5cb2124d29b457b2bb | 2022-06-29T17:46:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-CD-Modified-PubMedBERT-512 | 3 | null | transformers | 22,655 | Entry not found |
ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-512 | 10f147ca2745f1d86bac0b990eab9a90d635c9f1 | 2022-06-29T19:23:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Modified-PubMedBERT-512 | 3 | null | transformers | 22,656 | Entry not found |
ghadeermobasher/BioRed-CD-Original-PubMedBERT-512 | 75e162b89721b01ed5381b63cebf4a285ea9bb44 | 2022-06-29T18:09:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-CD-Original-PubMedBERT-512 | 3 | null | transformers | 22,657 | Entry not found |
ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512 | 57488196e41644ff9e5889e33f6c3be8d9a9d7d7 | 2022-06-29T19:29:35.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BioRed-Chem-Original-PubMedBERT-512 | 3 | null | transformers | 22,658 | Entry not found |
edbeeching/decision-transformer-gym-hopper-expert-new | 44a9b5ee3175c7c6af660c060e77e5b11f2d6f93 | 2022-06-29T19:11:44.000Z | [
"pytorch",
"decision_transformer",
"feature-extraction",
"transformers"
] | feature-extraction | false | edbeeching | null | edbeeching/decision-transformer-gym-hopper-expert-new | 3 | null | transformers | 22,659 | Entry not found |
Evelyn18/distilbert-base-uncased-becas-2 | dc861b2b7763a70a370ac12d3f88b01836367ddf | 2022-07-02T02:50:26.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-2 | 3 | null | transformers | 22,660 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-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. -->
# distilbert-base-uncased-becas-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9506
## 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: 0.1
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.9506 |
| No log | 2.0 | 10 | 5.9506 |
| No log | 3.0 | 15 | 5.9506 |
| No log | 4.0 | 20 | 5.9506 |
| No log | 5.0 | 25 | 5.9506 |
| No log | 6.0 | 30 | 5.9506 |
| No log | 7.0 | 35 | 5.9506 |
| No log | 8.0 | 40 | 5.9506 |
| No log | 9.0 | 45 | 5.9506 |
| No log | 10.0 | 50 | 5.9506 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
skpawar1305/wav2vec2-large-xlsr-53-german-finetuned-ks-de | a35a3ef1de356eee68ab1c2c5813655433be72e6 | 2022-06-30T02:18:47.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | skpawar1305 | null | skpawar1305/wav2vec2-large-xlsr-53-german-finetuned-ks-de | 3 | null | transformers | 22,661 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-xlsr-53-german-finetuned-ks-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-german-finetuned-ks-de
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8681
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 1.9490 | 0.0833 |
| No log | 2.0 | 2 | 1.9128 | 0.25 |
| No log | 3.0 | 3 | 1.8861 | 0.5833 |
| No log | 4.0 | 4 | 1.8681 | 0.6667 |
| No log | 5.0 | 5 | 1.8590 | 0.6667 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
andrescoro/my-three-class-sentiment-classification-RoBERTa | 819ff8447c524f354a2953c0214bc60cd1170109 | 2022-06-30T02:24:14.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | andrescoro | null | andrescoro/my-three-class-sentiment-classification-RoBERTa | 3 | null | transformers | 22,662 | Entry not found |
shahma/finetuned-bert-mrpc | 653009a0dcfb304d5962633a29d7e746ba9ff01f | 2022-06-30T16:00:47.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | shahma | null | shahma/finetuned-bert-mrpc | 3 | null | transformers | 22,663 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8602941176470589
- name: F1
type: f1
value: 0.9032258064516129
---
<!-- 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-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4266
- Accuracy: 0.8603
- F1: 0.9032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5475 | 1.0 | 230 | 0.4024 | 0.8211 | 0.8785 |
| 0.3309 | 2.0 | 460 | 0.3702 | 0.8529 | 0.8986 |
| 0.1716 | 3.0 | 690 | 0.4266 | 0.8603 | 0.9032 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
SivilTaram/tapex-t5-small-lm-adapt | 8a72ac33ba541513cab2960d168ec28f89f84b8a | 2022-06-30T08:49:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | SivilTaram | null | SivilTaram/tapex-t5-small-lm-adapt | 3 | null | transformers | 22,664 | ---
license: mit
---
|
SivilTaram/tapex-t5-large-finetuned-wtq | b750d9aa334ff274aeff7397490ddc2d21b5664a | 2022-06-30T09:04:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | SivilTaram | null | SivilTaram/tapex-t5-large-finetuned-wtq | 3 | null | transformers | 22,665 | ---
license: mit
---
|
asahi417/lmqg-mbart-large-cc25-itquad | fdb21e9cd1e264cebace34822bbb4e6f7264f7c9 | 2022-06-30T14:10:06.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mbart-large-cc25-itquad | 3 | null | transformers | 22,666 | Entry not found |
yaakov/test-distilbert-to-cola | e1b062bd9b3375d8a4b4738e0c863359a0ed2686 | 2022-06-30T15:43:07.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | yaakov | null | yaakov/test-distilbert-to-cola | 3 | null | transformers | 22,667 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: test-distilbert-to-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5443893754588841
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-distilbert-to-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5410
- Matthews Correlation: 0.5444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5244 | 1.0 | 535 | 0.5352 | 0.4122 |
| 0.348 | 2.0 | 1070 | 0.4897 | 0.5169 |
| 0.2315 | 3.0 | 1605 | 0.5410 | 0.5444 |
| 0.177 | 4.0 | 2140 | 0.7533 | 0.5177 |
| 0.1338 | 5.0 | 2675 | 0.8129 | 0.5384 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vishwasgautam/wav2vec2-base-libriSpeech-demo-colab | dc11a07be4bd81117d1a38be83da383b923eb485 | 2022-07-15T14:01:47.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vishwasgautam | null | vishwasgautam/wav2vec2-base-libriSpeech-demo-colab | 3 | null | transformers | 22,668 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-libriSpeech-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-libriSpeech-demo-colab
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: 0.4627
- Wer: 0.3174
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.2349 | 13.51 | 500 | 3.1154 | 1.0 |
| 1.5 | 27.03 | 1000 | 0.4627 | 0.3174 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
scottstots/roberta-base-prop-16-train-set | 5d8984b5c7595a41ecf73220e9e778f668f6c1f2 | 2022-07-22T20:18:31.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | scottstots | null | scottstots/roberta-base-prop-16-train-set | 3 | null | transformers | 22,669 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-prop-16-train-set
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-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-becas-0 | aa2db5a8c5202fc200c8655e61aa53466f1741de | 2022-07-01T18:34:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-0 | 3 | null | transformers | 22,670 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-0
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-becas-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2904
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.6445 |
| No log | 2.0 | 10 | 5.3875 |
| No log | 3.0 | 15 | 5.2904 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vishwasgautam/HuBERT-base-libriSpeech-demo-colab | cdfdf01605384a58680850c7dfbc488790203d53 | 2022-07-02T05:24:24.000Z | [
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vishwasgautam | null | vishwasgautam/HuBERT-base-libriSpeech-demo-colab | 3 | null | transformers | 22,671 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: HuBERT-base-libriSpeech-demo-colab
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. -->
# HuBERT-base-libriSpeech-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1456
- Wer: 0.2443
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.6395 | 13.51 | 500 | 3.1933 | 0.9930 |
| 2.5994 | 27.03 | 1000 | 0.1456 | 0.2443 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
gianlab/swin-tiny-patch4-window7-224-finetuned-skin-cancer | b3b174a3d59e2b1129afe0409fc432795c3c5545 | 2022-07-02T08:35:24.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | gianlab | null | gianlab/swin-tiny-patch4-window7-224-finetuned-skin-cancer | 3 | null | transformers | 22,672 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-skin-cancer
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7275449101796407
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-skin-cancer
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7695
- Accuracy: 0.7275
## Model description
This model was created by importing the dataset of the photos of skin cancer into Google Colab from kaggle here: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000 . I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb
The possible classified diseases are: 'Actinic-keratoses', 'Basal-cell-carcinoma', 'Benign-keratosis-like-lesions', 'Dermatofibroma', 'Melanocytic-nevi', 'Melanoma', 'Vascular-lesions' .
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6911 | 0.99 | 70 | 0.7695 | 0.7275 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
erickfm/leafy-sweep-1 | 5f0d06e2c447245986cb50e0a502393dc564bbc1 | 2022-07-02T11:31:16.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/leafy-sweep-1 | 3 | null | transformers | 22,673 | Entry not found |
erickfm/clear-sweep-1 | d77baf4a3f720b93808e94a3b29c29edef5841e9 | 2022-07-02T12:56:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/clear-sweep-1 | 3 | null | transformers | 22,674 | Entry not found |
erickfm/proud-sweep-1 | 0f94280b2326dad98f2a3722f76515e5c36f12b8 | 2022-07-03T01:32:22.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erickfm | null | erickfm/proud-sweep-1 | 3 | null | transformers | 22,675 | Entry not found |
haddadalwi/distilbert-base-uncased-finetuned-squad | a802049c339c7a2ab10da9c7daaa34cf5d58c57e | 2022-07-03T12:44:32.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | haddadalwi | null | haddadalwi/distilbert-base-uncased-finetuned-squad | 3 | null | transformers | 22,676 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5273
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 10 | 5.6821 |
| No log | 2.0 | 20 | 5.5273 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mf99/autotrain-sum-200-random-1082438930 | 8687184a5b2b800ebc936b8b3681446b0784098e | 2022-07-04T07:26:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:mf99/autotrain-data-sum-200-random",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | mf99 | null | mf99/autotrain-sum-200-random-1082438930 | 3 | null | transformers | 22,677 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- mf99/autotrain-data-sum-200-random
co2_eq_emissions: 4.994502035089263
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1082438930
- CO2 Emissions (in grams): 4.994502035089263
## Validation Metrics
- Loss: 0.44043827056884766
- Rouge1: 78.4534
- Rouge2: 73.6511
- RougeL: 78.2595
- RougeLsum: 78.2561
- Gen Len: 17.2448
## 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/mf99/autotrain-sum-200-random-1082438930
``` |
fce-m72109/mascorpus-bert-classifier | 26a4bfac1659b75c33cc643228c8c4c5c8b5f954 | 2022-07-03T22:36:48.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | fce-m72109 | null | fce-m72109/mascorpus-bert-classifier | 3 | null | transformers | 22,678 | ---
license: mit
---
|
dexay/f_ner_rober | deb3f5156ea75ce9fba6a5fd4369ccc2712b5da6 | 2022-07-03T22:54:41.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dexay | null | dexay/f_ner_rober | 3 | null | transformers | 22,679 | Entry not found |
asahi417/lmqg-mbart-large-cc25-ruquad | 5ddf3f3df125f35d32c2cd8b102b4f14cf291491 | 2022-07-04T04:41:48.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mbart-large-cc25-ruquad | 3 | null | transformers | 22,680 | Entry not found |
theojolliffe/t5-small-fb | 17f5acb44cf7c75daf52b7c19f7bb6f1cd16a2b1 | 2022-07-04T14:36:46.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/t5-small-fb | 3 | null | transformers | 22,681 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-fb
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. -->
# t5-small-fb
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 237 | 1.5946 | 50.8607 | 34.41 | 46.7706 | 48.1561 | 18.2917 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
asahi417/lmqg-mbart-large-cc25-frquad | 8dacbe150ca6166508b76fdda40fc10e07048775 | 2022-07-04T22:55:51.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | asahi417 | null | asahi417/lmqg-mbart-large-cc25-frquad | 3 | null | transformers | 22,682 | Entry not found |
Eleven/xlm-roberta-base-finetuned-panx-de-fr | 6bcfd60ea6acdacc4142cb349de39157ab97cf3c | 2022-07-05T15:59:42.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Eleven | null | Eleven/xlm-roberta-base-finetuned-panx-de-fr | 3 | null | transformers | 22,683 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1644
- F1: 0.8617
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sileod/roberta-base-random | 1c5602d5723c8aeb06f6362f2704a3e9bc000ba5 | 2022-07-05T18:05:49.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sileod | null | sileod/roberta-base-random | 3 | null | transformers | 22,684 | Entry not found |
Hamzaaa/wav2vec2-base-finetuned-Tess-finetuned-Tess | f554af3e8b200762120d767cfe78743bc02699ba | 2022-07-07T09:52:41.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers"
] | audio-classification | false | Hamzaaa | null | Hamzaaa/wav2vec2-base-finetuned-Tess-finetuned-Tess | 3 | null | transformers | 22,685 | Entry not found |
Hamzaaa/wav2vec2-base-finetuned-test-words | 8be34d0a3f62316191f731403687d96b55d74f46 | 2022-07-05T20:16:49.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers"
] | audio-classification | false | Hamzaaa | null | Hamzaaa/wav2vec2-base-finetuned-test-words | 3 | null | transformers | 22,686 | Entry not found |
tner/twitter-roberta-base-2019-90m-tweetner-2021 | 50e9c09d36d02824e389d2d0d79e5e804f955bca | 2022-07-07T10:11:33.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-2019-90m-tweetner-2021 | 3 | null | transformers | 22,687 | Entry not found |
tner/twitter-roberta-base-dec2020-tweetner-2021 | 05af0fe879d8cf192137aa3c547a46aad9fd9083 | 2022-07-07T10:12:08.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-dec2020-tweetner-2021 | 3 | null | transformers | 22,688 | Entry not found |
tner/twitter-roberta-base-2019-90m-tweetner-2020-2021-concat | 8d2df72dae413cedfaedcbe38bc030e21690dc9a | 2022-07-07T10:22:24.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-2019-90m-tweetner-2020-2021-concat | 3 | null | transformers | 22,689 | Entry not found |
tner/twitter-roberta-base-dec2020-tweetner-2020-2021-concat | aff67747652f0766ab7e575962faf295ca7a3e56 | 2022-07-07T10:23:30.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-dec2020-tweetner-2020-2021-concat | 3 | null | transformers | 22,690 | Entry not found |
tner/twitter-roberta-base-2019-90m-tweetner-2020-2021-continuous | b40f923cd42ebc3ccb9a40ece69eda38912c27b4 | 2022-07-11T22:17:17.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-2019-90m-tweetner-2020-2021-continuous | 3 | null | transformers | 22,691 | Entry not found |
tner/twitter-roberta-base-dec2020-tweetner-2020-2021-continuous | 79b68b98307a198fe230f0c29436ef6f1cb8d44f | 2022-07-11T23:54:27.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/twitter-roberta-base-dec2020-tweetner-2020-2021-continuous | 3 | null | transformers | 22,692 | Entry not found |
jonatasgrosman/exp_w2v2t_en_vp-sv_s179 | f672b01701ace6d5bbb3904896029453230a3f9b | 2022-07-08T06:02:23.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_en_vp-sv_s179 | 3 | null | transformers | 22,693 | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s179
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jourlin/wiki2json | 0540686bbca1a5c707db05190c581069d0c3ebe2 | 2022-07-08T11:46:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:opus_books",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | jourlin | null | jourlin/wiki2json | 3 | null | transformers | 22,694 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: wiki2json
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 4.8968
---
<!-- 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. -->
# wiki2json
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6848
- Bleu: 4.8968
- Gen Len: 17.6362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 1.9187 | 1.0 | 3178 | 1.6848 | 4.8968 | 17.6362 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_th_unispeech_s131 | 770beff8e799285a8d0eb5e7aa0908a9235480db | 2022-07-08T10:45:46.000Z | [
"pytorch",
"unispeech",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_th_unispeech_s131 | 3 | null | transformers | 22,695 | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech_s131
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_th_unispeech-ml_s256 | 8b0ad74d2fc498e48b510ffb9382903594d16cf4 | 2022-07-08T11:28:09.000Z | [
"pytorch",
"unispeech",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_th_unispeech-ml_s256 | 3 | null | transformers | 22,696 | ---
language:
- th
license: apache-2.0
tags:
- automatic-speech-recognition
- th
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_th_unispeech-ml_s256
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jk-gjom/autotrain-jk123-1105140277 | 7cf5f2adaf5b7bf7c44086fb810f1fdae9c75fa6 | 2022-07-08T13:22:03.000Z | [
"pytorch",
"bert",
"text-classification",
"unk",
"dataset:jk-gjom/autotrain-data-jk123",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | jk-gjom | null | jk-gjom/autotrain-jk123-1105140277 | 3 | null | transformers | 22,697 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- jk-gjom/autotrain-data-jk123
co2_eq_emissions: 0.1863935648335355
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1105140277
- CO2 Emissions (in grams): 0.1863935648335355
## Validation Metrics
- Loss: 0.0680043175816536
- Accuracy: 0.9808
- Macro F1: 0.9808013970263609
- Micro F1: 0.9808
- Weighted F1: 0.9808013970263609
- Macro Precision: 0.9808207901614748
- Micro Precision: 0.9808
- Weighted Precision: 0.9808207901614749
- Macro Recall: 0.9808
- Micro Recall: 0.9808
- Weighted Recall: 0.9808
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/jk-gjom/autotrain-jk123-1105140277
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("jk-gjom/autotrain-jk123-1105140277", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("jk-gjom/autotrain-jk123-1105140277", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
nielsr/videomae-base-finetuned-kinetics | 972cbe52fa2d39fc3aa7c4b9d7012a04bb7f094a | 2022-07-08T15:01:41.000Z | [
"pytorch",
"videomae",
"transformers"
] | null | false | nielsr | null | nielsr/videomae-base-finetuned-kinetics | 3 | null | transformers | 22,698 | Entry not found |
tner/bertweet-base-tweetner-2021 | abca2f4e997423cbdf33b8307de950f9f1cf4e57 | 2022-07-09T21:17:16.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/bertweet-base-tweetner-2021 | 3 | null | transformers | 22,699 | Entry not found |
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