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[model_cards] Migrate cards from this repo to model repos on huggingface.co (#9013) 



* rm all model cards

* Update the .rst

@sgugger it is still not super crystal clear/streamlined so let me know if any ideas to make it simpler

* Add a rootlevel README.md with simple instructions/context

* Update docs/source/model_sharing.rst
Co-authored-by: default avatarSylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: default avatarSylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>

* make style

* rm all model cards
Co-authored-by: default avatarSylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
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model_cards/T-Systems-onsite/german-roberta-sentence-transformer-v2/README.md deleted 100644 → 0
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---
language: de
license: mit
tags:
- sentence_embedding
- search
- pytorch
- xlm-roberta
- roberta
- xlm-r-distilroberta-base-paraphrase-v1
- paraphrase
datasets:
- STSbenchmark
metrics:
- Spearman’s rank correlation
- cosine similarity
---
# German RoBERTa for Sentence Embeddings V2
**The new [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) model is slightly better for German language. It is also the current best model for English language and works cross-lingually. Please consider using that model.**
This model is intended to [compute sentence (text embeddings)](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html) for German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to find sentences with a similar semantic meaning. For example this can be useful for [semantic textual similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html), [semantic search](https://www.sbert.net/docs/usage/semantic_search.html), or [paraphrase mining](https://www.sbert.net/docs/usage/paraphrase_mining.html). To do this you have to use the [Sentence Transformers Python framework](https://github.com/UKPLab/sentence-transformers).
> Sentence-BERT (SBERT) is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.
Source: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
This model is fine-tuned from [Philip May](https://eniak.de/) and open-sourced by [T-Systems-onsite](https://www.t-systems-onsite.de/). Special thanks to [Nils Reimers](https://www.nils-reimers.de/) for your awesome open-source work, the Sentence Transformers, the models and your help on GitHub.
## How to use
**The usage description above - provided by Hugging Face - is wrong for sentence embeddings! Please use this:**
To use this model install the `sentence-transformers` package (see here: <https://github.com/UKPLab/sentence-transformers>).
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('T-Systems-onsite/german-roberta-sentence-transformer-v2')
```
For details of usage and examples see here:
- [Computing Sentence Embeddings](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html)
- [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html)
- [Paraphrase Mining](https://www.sbert.net/docs/usage/paraphrase_mining.html)
- [Semantic Search](https://www.sbert.net/docs/usage/semantic_search.html)
- [Cross-Encoders](https://www.sbert.net/docs/usage/cross-encoder.html)
- [Examples on GitHub](https://github.com/UKPLab/sentence-transformers/tree/master/examples)
## Training
The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). This model has been further trained by [Nils Reimers](https://www.nils-reimers.de/) on a large scale paraphrase dataset for 50+ languages. [Nils Reimers](https://www.nils-reimers.de/) about this [on GitHub](https://github.com/UKPLab/sentence-transformers/issues/509#issuecomment-712243280):
>A paper is upcoming for the paraphrase models.
>
>These models were trained on various datasets with Millions of examples for paraphrases, mainly derived from Wikipedia edit logs, paraphrases mined from Wikipedia and SimpleWiki, paraphrases from news reports, AllNLI-entailment pairs with in-batch-negative loss etc.
>
>In internal tests, they perform much better than the NLI+STSb models as they have see more and broader type of training data. NLI+STSb has the issue that they are rather narrow in their domain and do not contain any domain specific words / sentences (like from chemistry, computer science, math etc.). The paraphrase models has seen plenty of sentences from various domains.
>
>More details with the setup, all the datasets, and a wider evaluation will follow soon.
The resulting model called `xlm-r-distilroberta-base-paraphrase-v1` has been released here: <https://github.com/UKPLab/sentence-transformers/releases/tag/v0.3.8>
Building on this cross language model we fine-tuned it for German language on the [deepl.com](https://www.deepl.com/translator) dataset of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark).
We did an automatic hyperparameter search for 102 trials with [Optuna](https://github.com/optuna/optuna). Using 10-fold crossvalidation on the deepl.com test and dev dataset we found the following best hyperparameters:
- batch_size = 15
- num_epochs = 4
- lr = 2.2995320905210864e-05
- eps = 1.8979875906303792e-06
- weight_decay = 0.003314045812507563
- warmup_steps_proportion = 0.46141685205829014
The final model was trained with these hyperparameters on the combination of `sts_de_train.csv` and `sts_de_dev.csv`. The `sts_de_test.csv` was left for testing.
# Evaluation
The evaluation has been done on the test set of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). The code is available on [Colab](https://colab.research.google.com/drive/1aCWOqDQx953kEnQ5k4Qn7uiixokocOHv?usp=sharing). As the metric for evaluation we use the Spearman’s rank correlation between the cosine-similarity of the sentence embeddings and STSbenchmark labels.
| Model Name | Spearman rank correlation<br/>(German) |
|--------------------------------------|-------------------------------------|
| xlm-r-distilroberta-base-paraphrase-v1 | 0.8079 |
| xlm-r-100langs-bert-base-nli-stsb-mean-tokens | 0.8194 |
| xlm-r-bert-base-nli-stsb-mean-tokens | 0.8194 |
| **T-Systems-onsite/<br/>german-roberta-sentence-transformer-v2** | **0.8529** |
| **[T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer)** | **0.8550** |
model_cards/Tereveni-AI/gpt2-124M-uk-fiction/README.md deleted 100644 → 0
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---
language: uk
---
Note: **default code snippet above won't work** because we are using `AlbertTokenizer` with `GPT2LMHeadModel`, see [issue](https://github.com/huggingface/transformers/issues/4285).
## GPT2 124M Trained on Ukranian Fiction
### Training details
Model was trained on corpus of 4040 fiction books, 2.77 GiB in total.
Evaluation on [brown-uk](https://github.com/brown-uk/corpus) gives perplexity of 50.16.
### Example usage:
```python
from transformers import AlbertTokenizer, GPT2LMHeadModel
tokenizer = AlbertTokenizer.from_pretrained("Tereveni-AI/gpt2-124M-uk-fiction")
model = GPT2LMHeadModel.from_pretrained("Tereveni-AI/gpt2-124M-uk-fiction")
input_ids = tokenizer.encode("Но зла Юнона, суча дочка,", add_special_tokens=False, return_tensors='pt')
outputs = model.generate(
input_ids,
do_sample=True,
num_return_sequences=3,
max_length=50
)
for i, out in enumerate(outputs):
print("{}: {}".format(i, tokenizer.decode(out)))
```
Prints something like this:
```bash
0: Но зла Юнона, суча дочка, яка затьмарила всі її таємниці: І хто з'їсть її душу, той помре». І, не дочекавшись гніву богів, посунула в пітьму, щоб не бачити перед собою. Але, за
1: Но зла Юнона, суча дочка, і довела мене до божевілля. Але він не знав нічого. Після того як я його побачив, мені стало зле. Я втратив рівновагу. Але в мене не було часу на роздуми. Я вже втратив надію
2: Но зла Юнона, суча дочка, не нарікала нам! — раптом вигукнула Юнона. — Це ти, старий йолопе! — мовила вона, не перестаючи сміятись. — Хіба ти не знаєш, що мені подобається ходити з тобою?
```
\ No newline at end of file
model_cards/TurkuNLP/bert-base-finnish-cased-v1/README.md deleted 100644 → 0
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---
language: fi
---
## Quickstart
**Release 1.0** (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: [bert-base-finnish-cased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased-v1.zip)
* Uncased Finnish BERT Base: [bert-base-finnish-uncased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased-v1.zip)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. The model can be fine-tuned to achieve state-of-the-art results for various Finnish natural language processing tasks.
FinBERT features a custom 50,000 wordpiece vocabulary that has much better coverage of Finnish words than e.g. the previously released [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) models from Google:
| Vocabulary | Example |
|------------|---------|
| FinBERT | Suomessa vaihtuu kesän aikana sekä pääministeri että valtiovarain ##ministeri . |
| Multilingual BERT | Suomessa vai ##htuu kes ##än aikana sekä p ##ää ##minister ##i että valt ##io ##vara ##in ##minister ##i . |
FinBERT has been pre-trained for 1 million steps on over 3 billion tokens (24B characters) of Finnish text drawn from news, online discussion, and internet crawls. By contrast, Multilingual BERT was trained on Wikipedia texts, where the Finnish Wikipedia text is approximately 3% of the amount used to train FinBERT.
These features allow FinBERT to outperform not only Multilingual BERT but also all previously proposed models when fine-tuned for Finnish natural language processing tasks.
## Results
### Document classification
![learning curves for Yle and Ylilauta document classification](https://raw.githubusercontent.com/TurkuNLP/FinBERT/master/img/yle-ylilauta-curves.png)
FinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performance with [FastText](https://fasttext.cc/) included for reference.)
[[code](https://github.com/spyysalo/finbert-text-classification)][[Yle data](https://github.com/spyysalo/yle-corpus)] [[Ylilauta data](https://github.com/spyysalo/ylilauta-corpus)]
### Named Entity Recognition
Evaluation on FiNER corpus ([Ruokolainen et al 2019](https://arxiv.org/abs/1908.04212))
| Model | Accuracy |
|--------------------|----------|
| **FinBERT** | **92.40%** |
| Multilingual BERT | 90.29% |
| [FiNER-tagger](https://github.com/Traubert/FiNer-rules) (rule-based) | 86.82% |
(FiNER tagger results from [Ruokolainen et al. 2019](https://arxiv.org/pdf/1908.04212.pdf))
[[code](https://github.com/jouniluoma/keras-bert-ner)][[data](https://github.com/mpsilfve/finer-data)]
### Part of speech tagging
Evaluation on three Finnish corpora annotated with [Universal Dependencies](https://universaldependencies.org/) part-of-speech tags: the Turku Dependency Treebank (TDT), FinnTreeBank (FTB), and Parallel UD treebank (PUD)
| Model | TDT | FTB | PUD |
|-------------------|-------------|-------------|-------------|
| **FinBERT** | **98.23%** | **98.39%** | **98.08%** |
| Multilingual BERT | 96.97% | 95.87% | 97.58% |
[[code](https://github.com/spyysalo/bert-pos)][[data](http://hdl.handle.net/11234/1-2837)]
## Use with PyTorch
If you want to use the model with the huggingface/transformers library, follow the steps in [huggingface_transformers.md](https://github.com/TurkuNLP/FinBERT/blob/master/huggingface_transformers.md)
## Previous releases
### Release 0.2
**October 24, 2019** Beta version of the BERT base uncased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-uncased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased.zip)
### Release 0.1
**September 30, 2019** We release a beta version of the BERT base cased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-cased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased.zip)
model_cards/TurkuNLP/bert-base-finnish-uncased-v1/README.md deleted 100644 → 0
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---
language: fi
---
## Quickstart
**Release 1.0** (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: [bert-base-finnish-cased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased-v1.zip)
* Uncased Finnish BERT Base: [bert-base-finnish-uncased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased-v1.zip)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. The model can be fine-tuned to achieve state-of-the-art results for various Finnish natural language processing tasks.
FinBERT features a custom 50,000 wordpiece vocabulary that has much better coverage of Finnish words than e.g. the previously released [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) models from Google:
| Vocabulary | Example |
|------------|---------|
| FinBERT | Suomessa vaihtuu kesän aikana sekä pääministeri että valtiovarain ##ministeri . |
| Multilingual BERT | Suomessa vai ##htuu kes ##än aikana sekä p ##ää ##minister ##i että valt ##io ##vara ##in ##minister ##i . |
FinBERT has been pre-trained for 1 million steps on over 3 billion tokens (24B characters) of Finnish text drawn from news, online discussion, and internet crawls. By contrast, Multilingual BERT was trained on Wikipedia texts, where the Finnish Wikipedia text is approximately 3% of the amount used to train FinBERT.
These features allow FinBERT to outperform not only Multilingual BERT but also all previously proposed models when fine-tuned for Finnish natural language processing tasks.
## Results
### Document classification
![learning curves for Yle and Ylilauta document classification](https://raw.githubusercontent.com/TurkuNLP/FinBERT/master/img/yle-ylilauta-curves.png)
FinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performance with [FastText](https://fasttext.cc/) included for reference.)
[[code](https://github.com/spyysalo/finbert-text-classification)][[Yle data](https://github.com/spyysalo/yle-corpus)] [[Ylilauta data](https://github.com/spyysalo/ylilauta-corpus)]
### Named Entity Recognition
Evaluation on FiNER corpus ([Ruokolainen et al 2019](https://arxiv.org/abs/1908.04212))
| Model | Accuracy |
|--------------------|----------|
| **FinBERT** | **92.40%** |
| Multilingual BERT | 90.29% |
| [FiNER-tagger](https://github.com/Traubert/FiNer-rules) (rule-based) | 86.82% |
(FiNER tagger results from [Ruokolainen et al. 2019](https://arxiv.org/pdf/1908.04212.pdf))
[[code](https://github.com/jouniluoma/keras-bert-ner)][[data](https://github.com/mpsilfve/finer-data)]
### Part of speech tagging
Evaluation on three Finnish corpora annotated with [Universal Dependencies](https://universaldependencies.org/) part-of-speech tags: the Turku Dependency Treebank (TDT), FinnTreeBank (FTB), and Parallel UD treebank (PUD)
| Model | TDT | FTB | PUD |
|-------------------|-------------|-------------|-------------|
| **FinBERT** | **98.23%** | **98.39%** | **98.08%** |
| Multilingual BERT | 96.97% | 95.87% | 97.58% |
[[code](https://github.com/spyysalo/bert-pos)][[data](http://hdl.handle.net/11234/1-2837)]
## Use with PyTorch
If you want to use the model with the huggingface/transformers library, follow the steps in [huggingface_transformers.md](https://github.com/TurkuNLP/FinBERT/blob/master/huggingface_transformers.md)
## Previous releases
### Release 0.2
**October 24, 2019** Beta version of the BERT base uncased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-uncased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased.zip)
### Release 0.1
**September 30, 2019** We release a beta version of the BERT base cased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data.
Download the model here: [bert-base-finnish-cased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased.zip)
model_cards/TypicaAI/magbert-ner/README.md deleted 100644 → 0
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---
language: fr
widget:
- text: "Je m'appelle Hicham et je vis a Fès"
---
# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)
## Introduction
[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based the language model for French Camembert (based on the RoBERTa architecture).
For further information or requests, please visite our website at [typica.ai Website](https://typica.ai/) or send us an email at contactus@typica.ai
## How to use MagBERT-NER with HuggingFace
##### Load MagBERT-NER and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("TypicaAI/magbert-ner")
model = AutoModelForTokenClassification.from_pretrained("TypicaAI/magbert-ner")
##### Process text sample (from wikipedia about the current Prime Minister of Morocco) Using NER pipeline
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)
nlp("Saad Dine El Otmani, né le 16 janvier 1956 à Inezgane, est un homme d'État marocain, chef du gouvernement du Maroc depuis le 5 avril 2017")
#[{'entity_group': 'I-PERSON',
# 'score': 0.8941445276141167,
# 'word': 'Saad Dine El Otmani'},
# {'entity_group': 'B-DATE',
# 'score': 0.5967703461647034,
# 'word': '16 janvier 1956'},
# {'entity_group': 'B-GPE', 'score': 0.7160899192094803, 'word': 'Inezgane'},
# {'entity_group': 'B-NORP', 'score': 0.7971733212471008, 'word': 'marocain'},
# {'entity_group': 'B-GPE', 'score': 0.8921478390693665, 'word': 'Maroc'},
# {'entity_group': 'B-DATE',
# 'score': 0.5760444005330404,
# 'word': '5 avril 2017'}]
```
## Authors
MagBert-NER Model was trained by Hicham Assoudi, Ph.D.
For any questions, comments you can contact me at assoudi@typica.ai
## Citation
If you use our work, please cite:
Hicham Assoudi, Ph.D., MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb), (2020)
model_cards/Vamsi/T5_Paraphrase_Paws/README.md deleted 100644 → 0
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---
language: "en"
tags:
- paraphrase-generation
- text-generation
- Conditional Generation
inference: false
---
​
# Paraphrase-Generation
​
## Model description
​
T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
​
## How to use
​
PyTorch and TF models available
​
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
​
tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")
​
sentence = "This is something which i cannot understand at all"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
​
```
For more reference on training your own T5 model or using this model, do check out [Paraphrase Generation](https://github.com/Vamsi995/Paraphrase-Generator).
model_cards/VictorSanh/roberta-base-finetuned-yelp-polarity/README.md deleted 100644 → 0
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---
language: en
datasets:
- yelp_polarity
---
# RoBERTa-base-finetuned-yelp-polarity
This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the test set.
## Hyper-parameters
We used the following hyper-parameters to train the model on one GPU:
```python
num_train_epochs = 2.0
learning_rate = 1e-05
weight_decay = 0.0
adam_epsilon = 1e-08
max_grad_norm = 1.0
per_device_train_batch_size = 32
gradient_accumulation_steps = 1
warmup_steps = 3500
seed = 42
```
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