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text2text-generation | transformers |
<!-- 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. -->
# opus-mt-en-de-finetuned-en-to-de
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6798
- Bleu: 26.4396
- Gen Len: 24.8156
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 2.0864 | 1.0 | 568611 | 1.6798 | 26.4396 | 24.8156 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.0.dev20210415+cu101
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-de-finetuned-en-to-de", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "de-en"}, "metrics": [{"type": "bleu", "value": 26.4396, "name": "Bleu"}]}]}]} | afreireosorio/opus-mt-en-de-finetuned-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | afshin/test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | aga11313/test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | ageron/distilbert-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | aggb/DialogGPT-medium-AGGB-B | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers |
# aggb DialogGPT spanish model | {"tags": ["conversational"]} | aggb/DialogGPT-small-AGGB-B | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers | bert-base-uncased model trained on the tobacco800 dataset for the task of page-stream-segmentation.
[Link](https://github.com/agiagoulas/page-stream-segmentation) to the GitHub Repo with the model implementation. | {} | agiagoulas/bert-pss | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aguilara42/audacitorch-tester | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null |
# Text to Speech Model
## Being used for the `Audio Labeler` effect in Audacity
metadata:
```
{
metadata = {
'sample_rate': 16000,
'domain_tags': ['speech'],
'short_description': 'I will label your speech into text :]',
'long_description':
'This is an Audacity wrapper for the model, '
'forked from the repository '
'facebook/s2t-medium-librispeech-asr'
'This model was trained by Changhan Wang'
'and Yun Tang and Xutai Ma and Anne Wu'
'and Dmytro Okhonko and Juan Pino.',
'tags': ['speech-to-text'],
'effect_type': 'waveform-to-labels',
'multichannel': False,
'labels': ["<pad>", "<s>", "</s>", "<unk>", "|", "E", "T", "A", "O", "N", "I", "H", "S", "R", "D", "L", "U", "M", "W", "C", "F", "G", "Y", "P", "B", "V", "K", "'", "X", "J", "Q", "Z"],
}
``` | {"tags": ["audacity"], "inference": false} | aguilara42/audacity-Wav2Vec2-Base | null | [
"audacity",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aguilara42/audacity-multilabel-test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null |
# Labeler With Timestamps
## Being used for the `Audio Labeler` effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
```
{
"sample_rate": 48000,
"domain_tags": ["Music"],
"tags": ["Audio Labeler"],
"effect_type": "waveform-to-labels",
"multichannel": false,
"labels": ["Acoustic Guitar", "Auxiliary Percussion", "Brass", "Clean Electric Guitar", "Distorted Electric Guitar", "Double Bass", "Drum Set", "Electric Bass", "Flute", "piano", "Reeds", "Saxophone", "Strings", "Trumpet", "Voice"],
"short_description": "Use me to label some instruments!",
"long_description": "An audio labeler, which outputs label predictions and time ranges for the labels. This model can label various instruments listed in the labels section."
}
``` | {"tags": ["audacity"], "inference": false} | aguilara42/openl3-labeler-w-timestamps | null | [
"audacity",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aguilara42/simple-vad | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | agungbesti/a | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | agungbesti/protas | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ahanadeb/wav2vec2-large-indian-instrument-classification-5sec | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | Hello World! | {} | ahanadeb/wav2vec2-large-indian-instrument-classification-v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | transformers | {} | ahanadeb/wav2vec2-large-indian-instrument-emotion-classification-v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | ahazeemi/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aheba31/blablabal | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as well.
It is trained on Voxceleb 1+ Voxceleb2 training data.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The model performance on Voxceleb1-test set(Cleaned) is:
| Release | EER(%) | minDCF |
|:-------------:|:--------------:|:--------------:|
| 05-03-21 | 0.69 | 0.08258 |
## Pipeline description
This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
gh repo clone aheba/speechbrain-aheba-contribs
git checkout pretrain_new
pip install -r requirements.txt
pip install --editable .
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Compute your speaker embeddings
```python
import torchaudio
from speechbrain.pretrained import Predictor
classifier = Predictor.import_model(source="aheba31/test-predictor")
signal, fs = torchaudio.load('samples/audio_samples/example1.wav')
embeddings = classifier.encode_batch(signal)
```
### Perform Speaker Verification
```python
from speechbrain.pretrained import SpeakerRecognition
verification = SpeakerRecognition.from_hparams(source="aheba31/test-predictor", savedir="aheba31/test-predictor")
score, prediction = verification.verify_files("speechbrain/spkrec-ecapa-voxceleb/example1.wav", "speechbrain/spkrec-ecapa-voxceleb/example2.flac")
```
The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain (aa018540).
To train it from scratch follows these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```
cd recipes/VoxCeleb/SpeakerRec
python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Referencing ECAPA-TDNN
```
@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
author = {Brecht Desplanques and
Jenthe Thienpondt and
Kris Demuynck},
editor = {Helen Meng and
Bo Xu and
Thomas Fang Zheng},
title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
in {TDNN} Based Speaker Verification},
booktitle = {Interspeech 2020},
pages = {3830--3834},
publisher = {{ISCA}},
year = {2020},
}
```
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
| {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["voxceleb"], "metrics": ["EER"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav"}]} | aheba31/test-predictor | null | [
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aheba31/zaion-lvcsr | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Zaion
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as well.
It is trained on Voxceleb 1+ Voxceleb2 training data.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The model performance on Voxceleb1-test set(Cleaned) is:
## Pipeline description
This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
gh repo clone aheba/speechbrain-aheba-contribs
git checkout pretrain_new
pip install -r requirements.txt
pip install --editable .
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Compute your speaker embeddings
```python
import torch audio
import torch
from speechbrain.pretrained import Pretrained
classifier = Pretrained.import_model(source="aheba31/test-predictor", pymodule_file="inference.py" ,class_name="EncoderClassifier")
print(classifier.classify_file("/workspace/contributions/test/spkrec-ecapa-voxceleb/example1.wav"))
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain (aa018540).
To train it from scratch follows these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```
cd recipes/VoxCeleb/SpeakerRec
python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Referencing ECAPA-TDNN
```
@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
author = {Brecht Desplanques and
Jenthe Thienpondt and
Kris Demuynck},
editor = {Helen Meng and
Bo Xu and
Thomas Fang Zheng},
title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
in {TDNN} Based Speaker Verification},
booktitle = {Interspeech 2020},
pages = {3830--3834},
publisher = {{ISCA}},
year = {2020},
}
```
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/ | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["Zaion corpus"], "metrics": ["EER"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav"}]} | aheba31/zaion-speaker-ident | null | [
"transformers",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
fill-mask | transformers | {} | ahmedJaafari/DarBert | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
question-answering | transformers | {} | ahmedattia143/roberta_squadv1_base | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
feature-extraction | transformers | {} | ahmednasserswe/sentence_distilbert | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | ### FinancialBERT for Sentiment Analysis
[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model.
The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models.
More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining
### Training data
FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
### Fine-tuning hyper-parameters
- learning_rate = 2e-5
- batch_size = 32
- max_seq_length = 512
- num_train_epochs = 5
### Evaluation metrics
The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
| sentiment | precision | recall | f1-score | support |
| ------------- |:-------------:|:-------------:|:-------------:| -----:|
| negative | 0.96 | 0.97 | 0.97 | 58 |
| neutral | 0.98 | 0.99 | 0.98 | 279 |
| positive | 0.98 | 0.97 | 0.97 | 148 |
| macro avg | 0.97 | 0.98 | 0.98 | 485 |
| weighted avg | 0.98 | 0.98 | 0.98 | 485 |
### How to use
The model can be used thanks to Transformers pipeline for sentiment analysis.
```python
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3)
tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.",
"Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.",
"Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.",
]
results = nlp(sentences)
print(results)
[{'label': 'positive', 'score': 0.9998133778572083},
{'label': 'neutral', 'score': 0.9997822642326355},
{'label': 'negative', 'score': 0.9877365231513977}]
```
> Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)
| {"language": "en", "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "datasets": ["financial_phrasebank"], "widget": [{"text": "Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales."}, {"text": "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000."}, {"text": "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008."}]} | ahmedrachid/FinancialBERT-Sentiment-Analysis | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"dataset:financial_phrasebank",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
fill-mask | transformers | **FinancialBERT** is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from it without the necessity of the significant computational resources required to train the model.
The model was trained on a large corpus of financial texts:
- *TRC2-financial*: 1.8M news articles that were published by Reuters between 2008 and 2010.
- *Bloomberg News*: 400,000 articles between 2006 and 2013.
- *Corporate Reports*: 192,000 transcripts (10-K & 10-Q)
- *Earning Calls*: 42,156 documents.
More details on `FinancialBERT` can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining
> Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)
| {"language": "en", "tags": ["fill-mask"], "widget": [{"text": "Tesla remains one of the highest [MASK] stocks on the market. Meanwhile, Aurora Innovation is a pre-revenue upstart that shows promise."}, {"text": "Asian stocks [MASK] from a one-year low on Wednesday as U.S. share futures and oil recovered from the previous day's selloff, but uncertainty over the impact of the Omicron"}, {"text": "U.S. stocks were set to rise on Monday, led by [MASK] in Apple which neared $3 trillion in market capitalization, while investors braced for a Federal Reserve meeting later this week."}]} | ahmedrachid/FinancialBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ahmedtronic/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers |
#Bert2Bert Turkish Paraphrase Generation
#INISTA 2021
#Comparison of Turkish Paraphrase Generation Models
#Dataset
The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset.
Dataset [Link](https://drive.google.com/file/d/1-2l9EwIzXZ7fUkNW1vdeF3lzQp2pygp_/view?usp=sharing)
#How To Use
```python
from transformers import BertTokenizerFast,EncoderDecoderModel
tokenizer=BertTokenizerFast.from_pretrained("dbmdz/bert-base-turkish-cased")
model = EncoderDecoderModel.from_pretrained("ahmetbagci/bert2bert-turkish-paraphrase-generation")
text="son model arabalar çevreye daha mı az zarar veriyor?"
input_ids = tokenizer(text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
#sample output
#son model arabalar çevre için daha az zararlı mı?
```
#Cite
```bibtex
@INPROCEEDINGS{9548335,
author={Bağcı, Ahmet and Amasyali, Mehmet Fatih},
booktitle={2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)},
title={Comparison of Turkish Paraphrase Generation Models},
year={2021},
volume={},
number={},
pages={1-6},
doi={10.1109/INISTA52262.2021.9548335}
}
``` | {"language": ["tr"], "tags": ["paraphrasing", "encoder-decoder", "seq2seq", "bert"]} | ahmetbagci/bert2bert-turkish-paraphrase-generation | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"paraphrasing",
"seq2seq",
"bert",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers | ## Albert xxlarge version 1 language model fine-tuned on SQuAD2.0
### (updated 30Sept2020) with the following results:
```
exact: 86.11134506864315
f1: 89.35371214945009
total': 11873
HasAns_exact': 83.56950067476383
HasAns_f1': 90.06353312254078
HasAns_total': 5928
NoAns_exact': 88.64592094196804
NoAns_f1': 88.64592094196804
NoAns_total': 5945
best_exact': 86.11134506864315
best_exact_thresh': 0.0
best_f1': 89.35371214944985
best_f1_thresh': 0.0
```
### from script:
```
python ${EXAMPLES}/run_squad.py \
--model_type albert \
--model_name_or_path albert-xxlarge-v1 \
--do_train \
--do_eval \
--train_file ${SQUAD}/train-v2.0.json \
--predict_file ${SQUAD}/dev-v2.0.json \
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--max_steps 8144 \
--warmup_steps 814 \
--learning_rate 3e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_gpu_train_batch_size 6 \
--gradient_accumulation_steps 8 \
--per_gpu_eval_batch_size 48 \
--fp16 \
--fp16_opt_level O1 \
--threads 12 \
--logging_steps 50 \
--save_steps 3000 \
--overwrite_output_dir \
--output_dir ${MODEL_PATH}
```
### using the following software & system:
```
Transformers: 3.1.0
PyTorch: 1.6.0
TensorFlow: 2.3.1
Python: 3.8.1
OS: Linux-5.4.0-48-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```
| {} | ahotrod/albert_xxlargev1_squad2_512 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers | ## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
### with the following results:
```
"exact": 87.09677419354838,
"f1": 89.98343832723452,
"total": 11873,
"HasAns_exact": 84.66599190283401,
"HasAns_f1": 90.44759839056285,
"HasAns_total": 5928,
"NoAns_exact": 89.52060555088309,
"NoAns_f1": 89.52060555088309,
"NoAns_total": 5945,
"best_exact": 87.09677419354838,
"best_exact_thresh": 0.0,
"best_f1": 89.98343832723432,
"best_f1_thresh": 0.0
```
### from script:
```
python ${EXAMPLES}/run_squad.py \
--model_type electra \
--model_name_or_path google/electra-large-discriminator \
--do_train \
--do_eval \
--train_file ${SQUAD}/train-v2.0.json \
--predict_file ${SQUAD}/dev-v2.0.json \
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--warmup_steps 306 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--per_gpu_train_batch_size 8 \
--gradient_accumulation_steps 16 \
--per_gpu_eval_batch_size 128 \
--fp16 \
--fp16_opt_level O1 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--output_dir ${MODEL_PATH}
```
### using the following system & software:
```
Transformers: 2.11.0
PyTorch: 1.5.0
TensorFlow: 2.2.0
Python: 3.8.1
OS/Platform: Linux-5.3.0-59-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```
| {} | ahotrod/electra_large_discriminator_squad2_512 | null | [
"transformers",
"pytorch",
"tf",
"electra",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ahsan11/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers |
IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBART model to build natural language generation applications for Indian languages by finetuning the model with supervised training data for tasks like machine translation, summarization, question generation, etc. Some salient features of the IndicBART are:
<ul>
<li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, Telugu and English. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding. </li>
<li> Trained on large Indic language corpora (452 million sentences and 9 billion tokens) which also includes Indian English content. </li>
<li> All languages, except English, have been represented in Devanagari script to encourage transfer learning among the related languages. </li>
</ul>
You can read more about IndicBART in this <a href="https://arxiv.org/abs/2109.02903">paper</a>.
For detailed documentation, look here: https://github.com/AI4Bharat/indic-bart/ and https://indicnlp.ai4bharat.org/indic-bart/
# Pre-training corpus
We used the <a href="https://indicnlp.ai4bharat.org/corpora/">IndicCorp</a> data spanning 12 languages with 452 million sentences (9 billion tokens). The model was trained using the text-infilling objective used in mBART.
# Usage:
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBART", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/IndicBART", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/IndicBART")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/IndicBART")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input and outputs. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("I am a boy </s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[ 466, 1981, 80, 25573, 64001, 64004]])
out = tokenizer("<2hi> मैं एक लड़का हूँ </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[64006, 942, 43, 32720, 8384, 64001]])
# Note that if you use any language other than Hindi or Marathi, you should convert its script to Devanagari using the Indic NLP Library.
model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:])
# For loss
model_outputs.loss ## This is not label smoothed.
# For logits
model_outputs.logits
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model.eval() # Set dropouts to zero
model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # I am a boy
# Note that if your output language is not Hindi or Marathi, you should convert its script from Devanagari to the desired language using the Indic NLP Library.
# What if we mask?
inp = tokenizer("I am [MASK] </s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # I am happy
inp = tokenizer("मैं [MASK] हूँ </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # मैं जानता हूँ
inp = tokenizer("मला [MASK] पाहिजे </s> <2mr>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # मला ओळखलं पाहिजे
```
# Notes:
1. This is compatible with the latest version of transformers but was developed with version 4.3.2 so consider using 4.3.2 if possible.
2. While I have only shown how to get logits and loss and how to generate outputs, you can do pretty much everything the MBartForConditionalGeneration class can do as in https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartForConditionalGeneration
3. Note that the tokenizer I have used is based on sentencepiece and not BPE. Therefore, I used the AlbertTokenizer class and not the MBartTokenizer class.
4. If you wish to use any language written in a non-Devanagari script (except English), then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.
# Fine-tuning on a downstream task
1. If you wish to fine-tune this model, then you can do so using the <a href="https://github.com/prajdabre/yanmtt">YANMTT</a> toolkit, following the instructions <a href="https://github.com/AI4Bharat/indic-bart ">here</a>.
2. (Untested) Alternatively, you may use the official huggingface scripts for <a href="https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation">translation</a> and <a href="https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization">summarization</a>.
# Contributors
<ul>
<li> Raj Dabre </li>
<li> Himani Shrotriya </li>
<li> Anoop Kunchukuttan </li>
<li> Ratish Puduppully </li>
<li> Mitesh M. Khapra </li>
<li> Pratyush Kumar </li>
</ul>
# Paper
If you use IndicBART, please cite the following paper:
```
@misc{dabre2021indicbart,
title={IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages},
author={Raj Dabre and Himani Shrotriya and Anoop Kunchukuttan and Ratish Puduppully and Mitesh M. Khapra and Pratyush Kumar},
year={2021},
eprint={2109.02903},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# License
The model is available under the MIT License. | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "tags": ["multilingual", "nlp", "indicnlp"]} | ai4bharat/IndicBART | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"arxiv:2109.02903",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | transformers |
# IndicBERT
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R etc.) while it also achieves a performance on-par or better than these models.
The 12 languages covered by IndicBERT are: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
The code can be found [here](https://github.com/divkakwani/indic-bert). For more information, checkout our [project page](https://indicnlp.ai4bharat.org/) or our [paper](https://indicnlp.ai4bharat.org/papers/arxiv2020_indicnlp_corpus.pdf).
## Pretraining Corpus
We pre-trained indic-bert on AI4Bharat's monolingual corpus. The corpus has the following distribution of languages:
| Language | as | bn | en | gu | hi | kn | |
| ----------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------- |
| **No. of Tokens** | 36.9M | 815M | 1.34B | 724M | 1.84B | 712M | |
| **Language** | **ml** | **mr** | **or** | **pa** | **ta** | **te** | **all** |
| **No. of Tokens** | 767M | 560M | 104M | 814M | 549M | 671M | 8.9B |
## Evaluation Results
IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our [official repo](https://github.com/divkakwani/indic-bert)
#### IndicGLUE
Task | mBERT | XLM-R | IndicBERT
-----| ----- | ----- | ------
News Article Headline Prediction | 89.58 | 95.52 | **95.87**
Wikipedia Section Title Prediction| **73.66** | 66.33 | 73.31
Cloze-style multiple-choice QA | 39.16 | 27.98 | **41.87**
Article Genre Classification | 90.63 | 97.03 | **97.34**
Named Entity Recognition (F1-score) | **73.24** | 65.93 | 64.47
Cross-Lingual Sentence Retrieval Task | 21.46 | 13.74 | **27.12**
Average | 64.62 | 61.09 | **66.66**
#### Additional Tasks
Task | Task Type | mBERT | XLM-R | IndicBERT
-----| ----- | ----- | ------ | -----
BBC News Classification | Genre Classification | 60.55 | **75.52** | 74.60
IIT Product Reviews | Sentiment Analysis | 74.57 | **78.97** | 71.32
IITP Movie Reviews | Sentiment Analaysis | 56.77 | **61.61** | 59.03
Soham News Article | Genre Classification | 80.23 | **87.6** | 78.45
Midas Discourse | Discourse Analysis | 71.20 | **79.94** | 78.44
iNLTK Headlines Classification | Genre Classification | 87.95 | 93.38 | **94.52**
ACTSA Sentiment Analysis | Sentiment Analysis | 48.53 | 59.33 | **61.18**
Winograd NLI | Natural Language Inference | 56.34 | 55.87 | **56.34**
Choice of Plausible Alternative (COPA) | Natural Language Inference | 54.92 | 51.13 | **58.33**
Amrita Exact Paraphrase | Paraphrase Detection | **93.81** | 93.02 | 93.75
Amrita Rough Paraphrase | Paraphrase Detection | 83.38 | 82.20 | **84.33**
Average | | 69.84 | **74.42** | 73.66
\* Note: all models have been restricted to a max_seq_length of 128.
## Downloads
The model can be downloaded [here](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/models/indic-bert-v1.tar.gz). Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from [Huggingface](https://huggingface.co/ai4bharat/indic-bert).
## Citing
If you are using any of the resources, please cite the following article:
```
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
```
We would like to hear from you if:
- You are using our resources. Please let us know how you are putting these resources to use.
- You have any feedback on these resources.
## License
The IndicBERT code (and models) are released under the MIT License.
## Contributors
- Divyanshu Kakwani
- Anoop Kunchukuttan
- Gokul NC
- Satish Golla
- Avik Bhattacharyya
- Mitesh Khapra
- Pratyush Kumar
This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org).
## Contact
- Anoop Kunchukuttan ([[email protected]](mailto:[email protected]))
- Mitesh Khapra ([[email protected]](mailto:[email protected]))
- Pratyush Kumar ([[email protected]](mailto:[email protected]))
| {"language": ["as", "bn", "en", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": "mit", "datasets": ["AI4Bharat IndicNLP Corpora"]} | ai4bharat/indic-bert | null | [
"transformers",
"pytorch",
"albert",
"as",
"bn",
"en",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers | {} | aicast/bert_finetuning_test | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# recipe-improver
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:
- Train Loss: 2.5570
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5539, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 2.5570 | 0 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "recipe-improver", "results": []}]} | aidan-o-brien/recipe-improver | null | [
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | transformers | {} | aidan-plenert-macdonald/gpt2-lv | null | [
"transformers",
"tf",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | transformers | {} | aidan-plenert-macdonald/model_lv_custom | null | [
"transformers",
"tf",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | aidenz/bert | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9260
- Recall: 0.9384
- F1: 0.9322
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2545 | 1.0 | 878 | 0.0711 | 0.9096 | 0.9214 | 0.9154 | 0.9800 |
| 0.0555 | 2.0 | 1756 | 0.0593 | 0.9185 | 0.9356 | 0.9270 | 0.9827 |
| 0.0297 | 3.0 | 2634 | 0.0607 | 0.9260 | 0.9384 | 0.9322 | 0.9834 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9260322366968425, "name": "Precision"}, {"type": "recall", "value": 0.9383599955252265, "name": "Recall"}, {"type": "f1", "value": 0.9321553592265377, "name": "F1"}, {"type": "accuracy", "value": 0.9834146186474335, "name": "Accuracy"}]}]}]} | aidj/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aidj/ernie | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | aidj/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
automatic-speech-recognition | transformers |
<!-- 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. -->
# vivos_prj1tha
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7737
- Wer: 0.5128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0541 | 10.25 | 400 | 1.0293 | 0.7051 |
| 0.5514 | 20.51 | 800 | 0.7737 | 0.5128 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "vivos_prj1tha", "results": []}]} | aiface/vivos_prj1tha | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | aimiekhe/yummv1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | aimiekhe/yummv2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers | {} | ainize/GPT2-futurama-script | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ainize/KcELECTRA-base-nsmc | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
summarization | transformers | # BART base model fine-tuned on CNN Dailymail
- This model is a [bart-base model](https://huggingface.co/facebook/bart-base) fine-tuned on the [CNN/Dailymail summarization dataset](https://huggingface.co/datasets/cnn_dailymail) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
https://github.com/pytorch/fairseq/tree/master/examples/bart
## Usage
### Python Code
```python
from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
# Load Model and Tokenize
tokenizer = PreTrainedTokenizerFast.from_pretrained("ainize/bart-base-cnn")
model = BartForConditionalGeneration.from_pretrained("ainize/bart-base-cnn")
# Encode Input Text
input_text = '(CNN) -- South Korea launched an investigation Tuesday into reports of toxic chemicals being dumped at a former U.S. military base, the Defense Ministry said. The tests follow allegations of American soldiers burying chemicals on Korean soil. The first tests are being carried out by a joint military, government and civilian task force at the site of what was Camp Mercer, west of Seoul. "Soil and underground water will be taken in the areas where toxic chemicals were allegedly buried," said the statement from the South Korean Defense Ministry. Once testing is finished, the government will decide on how to test more than 80 other sites -- all former bases. The alarm was raised this month when a U.S. veteran alleged barrels of the toxic herbicide Agent Orange were buried at an American base in South Korea in the late 1970s. Two of his fellow soldiers corroborated his story about Camp Carroll, about 185 miles (300 kilometers) southeast of the capital, Seoul. "We\'ve been working very closely with the Korean government since we had the initial claims," said Lt. Gen. John Johnson, who is heading the Camp Carroll Task Force. "If we get evidence that there is a risk to health, we are going to fix it." A joint U.S.- South Korean investigation is being conducted at Camp Carroll to test the validity of allegations. The U.S. military sprayed Agent Orange from planes onto jungles in Vietnam to kill vegetation in an effort to expose guerrilla fighters. Exposure to the chemical has been blamed for a wide variety of ailments, including certain forms of cancer and nerve disorders. It has also been linked to birth defects, according to the Department of Veterans Affairs. Journalist Yoonjung Seo contributed to this report.'
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate Summary Text Ids
summary_text_ids = model.generate(
input_ids=input_ids,
bos_token_id=model.config.bos_token_id,
eos_token_id=model.config.eos_token_id,
length_penalty=2.0,
max_length=142,
min_length=56,
num_beams=4,
)
# Decoding Text
print(tokenizer.decode(summary_text_ids[0], skip_special_tokens=True))
```
### API
You can experience this model through [ainize](https://ainize.ai/gkswjdzz/summarize-torchserve?branch=main). | {"language": "en", "license": "apache-2.0", "tags": ["summarization", "bart"], "datasets": ["cnn_dailymail"]} | ainize/bart-base-cnn | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
feature-extraction | transformers |
Original repository : <https://huggingface.co/EleutherAI/gpt-j-6B> | {"license": "apache-2.0"} | ainize/gpt-j-6B-float16 | null | [
"transformers",
"pytorch",
"gptj",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers | {} | ainize/gpt2-mcu-script-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 1
Train runtime: 3.4982 secs
Loss: 3.0894
Training notebook: [Colab](https://colab.research.google.com/drive/1RawVxulLETFicWMY0YANUdP-H-e7Eeyc)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
| {} | ainize/gpt2-rnm-with-only-rick | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 3
Train runtime: 7.1779 secs
Loss: 2.5694
Training notebook: [Colab](https://colab.research.google.com/drive/12NvO1SIZevF8ybJqfN9O21I3i9bU1dOO#scrollTo=KUsyn02WWmf5)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
| {} | ainize/gpt2-rnm-with-season-1 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Fine tuning data 2: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
Base model: e-tony/gpt2-rnm
Epoch: 2
Train runtime: 790.0612 secs
Loss: 2.8569
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Rick-N-Morty-with-SpongeBob?branch=master)
Demo page: [End-point](https://master-gpt2-rick-n-morty-with-sponge-bob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
| {} | ainize/gpt2-rnm-with-spongebob | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers | {} | ainize/gpt2-simpsons-script-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | ### Model information
Fine tuning data: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
License: CC-BY-SA
Base model: gpt-2 large
Epoch: 50
Train runtime: 14723.0716 secs
Loss: 0.0268
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Spongebob?branch=master)
Demo page: [End-point](https://master-gpt2-spongebob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp) | {} | ainize/gpt2-spongebob-script-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
question-answering | transformers |
# bert-base for QA
**Code:** See [Ainize Workspace](https://link.ainize.ai/3FjvBVn)
**klue-bert-base-mrc DEMO**: [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/)
**klue-bert-base-mrc API**: [Ainize API](https://ainize.ai/scy6500/KLUE-MRC-BERT?branch=main)
## Overview
**Language model:** klue/bert-base
**Language:** Korean
**Downstream-task:** Extractive QA
**Training data:** KLUE-MRC
**Eval data:** KLUE-MRC
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-mrc")
model = AutoModelForQuestionAnswering.from_pretrained("ainize/klue-bert-base-mrc")
context = "your context"
question = "your question"
encodings = tokenizer(context, question, max_length=512, truncation=True,
padding="max_length", return_token_type_ids=False)
encodings = {key: torch.tensor([val]) for key, val in encodings.items()}
input_ids = encodings["input_ids"]
attention_mask = encodings["attention_mask"]
pred = model(input_ids, attention_mask=attention_mask)
start_logits, end_logits = pred.start_logits, pred.end_logits
token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
pred_ids = input_ids[0][token_start_index: token_end_index + 1]
prediction = tokenizer.decode(pred_ids)
```
## About us
[Teachable NLP](https://ainize.ai/teachable-nlp) - Train NLP models with your own text without writing any code
[Ainize](https://ainize.ai/) - Deploy ML project using free gpu
| {"language": "ko", "license": "cc-by-sa-4.0", "tags": ["bert", "mrc"], "datasets": ["klue"]} | ainize/klue-bert-base-mrc | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"mrc",
"ko",
"dataset:klue",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers | # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a>
- <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Github</a>
- Run KLUE RE on free GPU : <a href="https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ainize-team/klue-re-workspace">Ainize Workspace</a>
<br>
# Usage
<pre><code>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-re")
model = AutoModelForSequenceClassification.from_pretrained("ainize/klue-bert-base-re")
# Add "<subj>", "</subj>" to both ends of the subject object and "<obj>", "</obj>" to both ends of the object object.
sentence = "<subj>손흥민</subj>은 <obj>대한민국</obj>에서 태어났다."
encodings = tokenizer(sentence,
max_length=128,
truncation=True,
padding="max_length",
return_tensors="pt")
outputs = model(**encodings)
logits = outputs['logits']
preds = torch.argmax(logits, dim=1)
</code></pre>
<br>
# About us
- <a href="https://ainize.ai/teachable-nlp">Teachable NLP</a> - Train NLP models with your own text without writing any code
- <a href="https://ainize.ai/">Ainize</a> - Deploy ML project using free gpu | {} | ainize/klue-bert-base-re | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers | # kobart-news
- This model is a [kobart](https://huggingface.co/hyunwoongko/kobart) fine-tuned on the [문서요약 텍스트/신문기사](https://aihub.or.kr/aidata/8054) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp).
## Usage
### Python Code
```python
from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
# Load Model and Tokenize
tokenizer = PreTrainedTokenizerFast.from_pretrained("ainize/kobart-news")
model = BartForConditionalGeneration.from_pretrained("ainize/kobart-news")
# Encode Input Text
input_text = '국내 전반적인 경기침체로 상가 건물주의 수익도 전국적인 감소세를 보이고 있는 것으로 나타났다. 수익형 부동산 연구개발기업 상가정보연구소는 한국감정원 통계를 분석한 결과 전국 중대형 상가 순영업소득(부동산에서 발생하는 임대수입, 기타수입에서 제반 경비를 공제한 순소득)이 1분기 ㎡당 3만4200원에서 3분기 2만5800원으로 감소했다고 17일 밝혔다. 수도권, 세종시, 지방광역시에서 순영업소득이 가장 많이 감소한 지역은 3분기 1만3100원을 기록한 울산으로, 1분기 1만9100원 대비 31.4% 감소했다. 이어 대구(-27.7%), 서울(-26.9%), 광주(-24.9%), 부산(-23.5%), 세종(-23.4%), 대전(-21%), 경기(-19.2%), 인천(-18.5%) 순으로 감소했다. 지방 도시의 경우도 비슷했다. 경남의 3분기 순영업소득은 1만2800원으로 1분기 1만7400원 대비 26.4% 감소했으며 제주(-25.1%), 경북(-24.1%), 충남(-20.9%), 강원(-20.9%), 전남(-20.1%), 전북(-17%), 충북(-15.3%) 등도 감소세를 보였다. 조현택 상가정보연구소 연구원은 "올해 내수 경기의 침체된 분위기가 유지되며 상가, 오피스 등을 비롯한 수익형 부동산 시장의 분위기도 경직된 모습을 보였고 오피스텔, 지식산업센터 등의 수익형 부동산 공급도 증가해 공실의 위험도 늘었다"며 "실제 올 3분기 전국 중대형 상가 공실률은 11.5%를 기록하며 1분기 11.3% 대비 0.2% 포인트 증가했다"고 말했다. 그는 "최근 소셜커머스(SNS를 통한 전자상거래), 음식 배달 중개 애플리케이션, 중고 물품 거래 애플리케이션 등의 사용 증가로 오프라인 매장에 영향을 미쳤다"며 "향후 지역, 콘텐츠에 따른 상권 양극화 현상은 심화될 것으로 보인다"고 덧붙였다.'
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate Summary Text Ids
summary_text_ids = model.generate(
input_ids=input_ids,
bos_token_id=model.config.bos_token_id,
eos_token_id=model.config.eos_token_id,
length_penalty=2.0,
max_length=142,
min_length=56,
num_beams=4,
)
# Decoding Text
print(tokenizer.decode(summary_text_ids[0], skip_special_tokens=True))
```
### API and Demo
You can experience this model through [ainize-api](https://ainize.ai/gkswjdzz/summarize-torchserve?branch=main) and [ainize-demo](https://main-summarize-torchserve-gkswjdzz.endpoint.ainize.ai/).
| {"language": "ko", "license": "mit", "tags": ["summarization", "bart"]} | ainize/kobart-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | {} | airKlizz/bart-large-cnn-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/bart-large-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/bart-large-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/bart-large-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {"language": "fr", "license": "mit"} | airKlizz/bart-large-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/bert2bert-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/bert2bert-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {"language": "fr", "license": "mit"} | airKlizz/bert2bert-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-12-3-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-12-6-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-3-3-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-6-12-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-6-6-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/distilbart-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | airKlizz/gbert-base-germeval21-toxic-with-data-augmentation | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | airKlizz/gbert-base-germeval21-toxic | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/mt5-base-germeval21-toxic-with-data-augmentation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/mt5-base-germeval21-toxic-with-task-specific-pretraining-and-data-augmentation | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/mt5-base-germeval21-toxic-with-task-specific-pretraining | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/mt5-base-germeval21-toxic | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-all-languages
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2454
- Rouge1: 8.3826
- Rouge2: 3.5524
- Rougel: 6.8656
- Rougelsum: 7.8362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 3467 | 2.4034 | 8.0363 | 3.2484 | 6.5409 | 7.477 |
| No log | 2.0 | 6934 | 2.3276 | 8.1054 | 3.2905 | 6.5765 | 7.5687 |
| No log | 3.0 | 10401 | 2.2976 | 8.169 | 3.4272 | 6.6597 | 7.6435 |
| No log | 4.0 | 13868 | 2.2795 | 8.2941 | 3.5353 | 6.7881 | 7.7664 |
| 2.8057 | 5.0 | 17335 | 2.2621 | 8.3302 | 3.5599 | 6.8238 | 7.7928 |
| 2.8057 | 6.0 | 20802 | 2.2547 | 8.3818 | 3.5886 | 6.8672 | 7.844 |
| 2.8057 | 7.0 | 24269 | 2.2472 | 8.3809 | 3.5696 | 6.8575 | 7.8327 |
| 2.8057 | 8.0 | 27736 | 2.2454 | 8.3826 | 3.5524 | 6.8656 | 7.8362 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-all-languages", "results": []}]} | airKlizz/mt5-base-wikinewssum-all-languages | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english-100
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6225
- Rouge1: 3.909
- Rouge2: 0.9312
- Rougel: 3.3835
- Rougelsum: 3.7786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 0.96 | 12 | 14.4949 | 2.7398 | 0.7181 | 2.491 | 2.6561 |
| No log | 1.96 | 24 | 10.5056 | 4.4428 | 1.4293 | 3.8469 | 4.2869 |
| No log | 2.96 | 36 | 8.9856 | 4.1179 | 1.229 | 3.5726 | 3.9693 |
| No log | 3.96 | 48 | 7.7950 | 3.9217 | 1.1339 | 3.4256 | 3.7905 |
| No log | 4.96 | 60 | 7.0734 | 3.8004 | 1.0326 | 3.3246 | 3.6766 |
| No log | 5.96 | 72 | 6.7897 | 3.6351 | 0.9162 | 3.1839 | 3.5149 |
| No log | 6.96 | 84 | 6.6610 | 3.7486 | 0.8829 | 3.2583 | 3.6193 |
| No log | 7.96 | 96 | 6.6225 | 3.909 | 0.9312 | 3.3835 | 3.7786 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english-100", "results": []}]} | airKlizz/mt5-base-wikinewssum-english-100 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english-1000
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4724
- Rouge1: 7.7389
- Rouge2: 3.1606
- Rougel: 6.3317
- Rougelsum: 7.2487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 125 | 2.6981 | 7.1504 | 2.6253 | 5.8261 | 6.7427 |
| No log | 2.0 | 250 | 2.5597 | 7.4666 | 2.9362 | 6.0965 | 6.9699 |
| No log | 3.0 | 375 | 2.5145 | 7.4599 | 2.9449 | 6.0941 | 6.9734 |
| No log | 4.0 | 500 | 2.4904 | 7.5063 | 2.975 | 6.137 | 7.0027 |
| No log | 5.0 | 625 | 2.4904 | 7.6027 | 3.0582 | 6.2161 | 7.0832 |
| No log | 6.0 | 750 | 2.4801 | 7.7601 | 3.1916 | 6.3689 | 7.2686 |
| No log | 7.0 | 875 | 2.4737 | 7.7162 | 3.1332 | 6.3113 | 7.2283 |
| No log | 8.0 | 1000 | 2.4724 | 7.7389 | 3.1606 | 6.3317 | 7.2487 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english-1000", "results": []}]} | airKlizz/mt5-base-wikinewssum-english-1000 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3040
- Rouge1: 8.9565
- Rouge2: 3.6563
- Rougel: 7.1346
- Rougelsum: 8.3802
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 1010 | 2.4360 | 8.7287 | 3.5817 | 7.0093 | 8.1879 |
| No log | 2.0 | 2020 | 2.3922 | 8.7227 | 3.5385 | 6.96 | 8.1887 |
| No log | 3.0 | 3030 | 2.3422 | 8.8565 | 3.5772 | 7.0203 | 8.2957 |
| No log | 4.0 | 4040 | 2.3288 | 8.89 | 3.645 | 7.0602 | 8.3314 |
| 3.1253 | 5.0 | 5050 | 2.3209 | 8.868 | 3.6109 | 7.0537 | 8.299 |
| 3.1253 | 6.0 | 6060 | 2.3127 | 8.9488 | 3.6615 | 7.1044 | 8.3785 |
| 3.1253 | 7.0 | 7070 | 2.3056 | 8.9366 | 3.6507 | 7.1338 | 8.3615 |
| 3.1253 | 8.0 | 8080 | 2.3040 | 8.9565 | 3.6563 | 7.1346 | 8.3802 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english", "results": []}]} | airKlizz/mt5-base-wikinewssum-english | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-french
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0917
- Rouge1: 12.0984
- Rouge2: 5.7289
- Rougel: 9.9245
- Rougelsum: 11.0697
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|
| No log | 1.0 | 549 | 2.3203 | 11.5172 | 4.9352 | 9.3617 | 10.4605 |
| No log | 2.0 | 1098 | 2.2057 | 11.8469 | 5.2369 | 9.6452 | 10.8337 |
| No log | 3.0 | 1647 | 2.1525 | 11.9096 | 5.4027 | 9.7648 | 10.9315 |
| 3.1825 | 4.0 | 2196 | 2.1307 | 12.0782 | 5.5848 | 9.9614 | 11.1081 |
| 3.1825 | 5.0 | 2745 | 2.1172 | 11.9821 | 5.6042 | 9.8216 | 11.0077 |
| 3.1825 | 6.0 | 3294 | 2.1012 | 12.0845 | 5.6834 | 9.9119 | 11.0741 |
| 3.1825 | 7.0 | 3843 | 2.0964 | 12.1296 | 5.7271 | 9.9495 | 11.1227 |
| 2.3376 | 8.0 | 4392 | 2.0917 | 12.0984 | 5.7289 | 9.9245 | 11.0697 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-french", "results": []}]} | airKlizz/mt5-base-wikinewssum-french | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-german
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5135
- Rouge1: 8.0553
- Rouge2: 2.7846
- Rougel: 6.2182
- Rougelsum: 7.6203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 723 | 2.7112 | 7.3681 | 2.3679 | 5.5705 | 6.7588 |
| No log | 2.0 | 1446 | 2.6178 | 7.8539 | 2.7551 | 6.2081 | 7.4139 |
| No log | 3.0 | 2169 | 2.5756 | 7.8401 | 2.6075 | 6.0135 | 7.4303 |
| No log | 4.0 | 2892 | 2.5465 | 8.1097 | 2.8525 | 6.268 | 7.6482 |
| 3.4589 | 5.0 | 3615 | 2.5315 | 8.0192 | 2.7848 | 6.2484 | 7.5859 |
| 3.4589 | 6.0 | 4338 | 2.5222 | 8.1063 | 2.8986 | 6.337 | 7.6564 |
| 3.4589 | 7.0 | 5061 | 2.5136 | 8.0565 | 2.8707 | 6.2732 | 7.6105 |
| 3.4589 | 8.0 | 5784 | 2.5135 | 8.0553 | 2.7846 | 6.2182 | 7.6203 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-german", "results": []}]} | airKlizz/mt5-base-wikinewssum-german | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-italian
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.5739
- Rouge1: 2.1728
- Rouge2: 0.1516
- Rougel: 2.0846
- Rougelsum: 2.0515
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 8 | 16.6193 | 2.4011 | 0.3829 | 2.1505 | 2.2161 |
| No log | 2.0 | 16 | 15.8909 | 2.5165 | 0.2799 | 2.3403 | 2.3523 |
| No log | 3.0 | 24 | 15.4843 | 2.2794 | 0.2252 | 2.1849 | 2.1382 |
| 17.2559 | 4.0 | 32 | 13.0850 | 2.2448 | 0.1516 | 2.1426 | 2.0859 |
| 17.2559 | 5.0 | 40 | 11.7838 | 2.2448 | 0.1516 | 2.1426 | 2.0859 |
| 17.2559 | 6.0 | 48 | 11.3207 | 2.2424 | 0.1516 | 2.1423 | 2.1171 |
| 17.2559 | 7.0 | 56 | 10.7871 | 2.1081 | 0.1516 | 2.0227 | 1.9838 |
| 14.6026 | 8.0 | 64 | 10.5739 | 2.1728 | 0.1516 | 2.0846 | 2.0515 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-italian", "results": []}]} | airKlizz/mt5-base-wikinewssum-italian | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-polish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3179
- Rouge1: 7.911
- Rouge2: 3.2189
- Rougel: 6.7856
- Rougelsum: 7.4485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 315 | 2.5391 | 5.9874 | 2.3594 | 5.1303 | 5.6116 |
| No log | 2.0 | 630 | 2.4446 | 7.7294 | 3.0152 | 6.6024 | 7.2757 |
| No log | 3.0 | 945 | 2.3912 | 7.6451 | 2.9785 | 6.5714 | 7.2011 |
| 3.5311 | 4.0 | 1260 | 2.3720 | 7.8007 | 3.0913 | 6.7067 | 7.3451 |
| 3.5311 | 5.0 | 1575 | 2.3411 | 7.8374 | 3.1208 | 6.7288 | 7.3459 |
| 3.5311 | 6.0 | 1890 | 2.3354 | 7.8664 | 3.1655 | 6.762 | 7.4364 |
| 3.5311 | 7.0 | 2205 | 2.3175 | 7.9529 | 3.2225 | 6.8438 | 7.4904 |
| 2.692 | 8.0 | 2520 | 2.3179 | 7.911 | 3.2189 | 6.7856 | 7.4485 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-polish", "results": []}]} | airKlizz/mt5-base-wikinewssum-polish | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-portuguese
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0428
- Rouge1: 9.4966
- Rouge2: 4.2224
- Rougel: 7.9845
- Rougelsum: 8.8641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 334 | 2.2258 | 7.3686 | 2.9066 | 6.3167 | 6.8758 |
| No log | 2.0 | 668 | 2.1389 | 9.0551 | 3.8395 | 7.6578 | 8.4641 |
| No log | 3.0 | 1002 | 2.1030 | 9.2792 | 3.9352 | 7.8259 | 8.663 |
| No log | 4.0 | 1336 | 2.0841 | 9.337 | 4.0647 | 7.8662 | 8.693 |
| 3.2831 | 5.0 | 1670 | 2.0487 | 9.4244 | 4.0821 | 7.8633 | 8.7111 |
| 3.2831 | 6.0 | 2004 | 2.0580 | 9.4598 | 4.1598 | 7.9511 | 8.8299 |
| 3.2831 | 7.0 | 2338 | 2.0426 | 9.501 | 4.1885 | 7.9803 | 8.8612 |
| 3.2831 | 8.0 | 2672 | 2.0428 | 9.4966 | 4.2224 | 7.9845 | 8.8641 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-portuguese", "results": []}]} | airKlizz/mt5-base-wikinewssum-portuguese | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-spanish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2394
- Rouge1: 7.9732
- Rouge2: 3.5041
- Rougel: 6.6713
- Rougelsum: 7.5229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 528 | 2.3707 | 6.687 | 2.9169 | 5.6793 | 6.2978 |
| No log | 2.0 | 1056 | 2.3140 | 7.9518 | 3.4529 | 6.7265 | 7.4984 |
| No log | 3.0 | 1584 | 2.2848 | 7.9708 | 3.5344 | 6.7272 | 7.534 |
| No log | 4.0 | 2112 | 2.2668 | 8.0252 | 3.5323 | 6.7319 | 7.5819 |
| 3.2944 | 5.0 | 2640 | 2.2532 | 8.0143 | 3.534 | 6.7155 | 7.582 |
| 3.2944 | 6.0 | 3168 | 2.2399 | 7.9525 | 3.4849 | 6.6716 | 7.5155 |
| 3.2944 | 7.0 | 3696 | 2.2376 | 7.9405 | 3.4661 | 6.6559 | 7.5043 |
| 3.2944 | 8.0 | 4224 | 2.2394 | 7.9732 | 3.5041 | 6.6713 | 7.5229 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-spanish", "results": []}]} | airKlizz/mt5-base-wikinewssum-spanish | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-wikinewssum-test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9354
- Rouge1: 6.8433
- Rouge2: 2.5498
- Rougel: 5.6114
- Rougelsum: 6.353
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 661 | 3.2810 | 6.4161 | 2.403 | 5.3674 | 6.0329 |
| No log | 2.0 | 1322 | 3.1515 | 6.9291 | 2.6826 | 5.6839 | 6.4359 |
| No log | 3.0 | 1983 | 3.0565 | 6.7939 | 2.6113 | 5.6133 | 6.3126 |
| No log | 4.0 | 2644 | 2.9815 | 6.0279 | 2.1637 | 4.9892 | 5.5962 |
| No log | 5.0 | 3305 | 2.9645 | 6.3926 | 2.339 | 5.2716 | 5.9443 |
| 3.9937 | 6.0 | 3966 | 2.9476 | 6.4739 | 2.3615 | 5.3473 | 6.0089 |
| 3.9937 | 7.0 | 4627 | 2.9405 | 6.615 | 2.4309 | 5.4493 | 6.1445 |
| 3.9937 | 8.0 | 5288 | 2.9354 | 6.8433 | 2.5498 | 5.6114 | 6.353 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-wikinewssum-test", "results": []}]} | airKlizz/mt5-small-wikinewssum-test | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | {} | airKlizz/t5-base-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/t5-base-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/t5-base-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {"language": "fr", "license": "mit"} | airKlizz/t5-base-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/t5-base-with-title-multi-de-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/t5-base-with-title-multi-en-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {"language": "fr", "license": "mit"} | airKlizz/t5-base-with-title-multi-fr-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | {} | airKlizz/t5-small-multi-combine-wiki-news | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | airKlizz/xlm-roberta-base-germeval21-toxic-with-data-augmentation | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | airKlizz/xlm-roberta-base-germeval21-toxic-with-task-specific-pretraining-and-data-augmentation | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers | {} | airKlizz/xlm-roberta-base-germeval21-toxic-with-task-specific-pretraining | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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