modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 18:52:31
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-02 18:52:05
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
gagan3012/keytotext-small
|
gagan3012
| 2021-03-11T23:33:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gagan3012/keytotext-small
#### Usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/keytotext-small")
model = AutoModelWithLMHead.from_pretrained("gagan3012/keytotext-small")
```
### Demo:
[](https://share.streamlit.io/gagan3012/keytotext/app.py)
https://share.streamlit.io/gagan3012/keytotext/app.py
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
gagan3012/keytotext
|
gagan3012
| 2021-03-11T20:23:32Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gagan3012/keytotext-small
#### Usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/keytotext-small")
model = AutoModelWithLMHead.from_pretrained("gagan3012/keytotext-small")
```
### Demo:
[](https://share.streamlit.io/gagan3012/keytotext/app.py)
https://share.streamlit.io/gagan3012/keytotext/app.py
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
navteca/quora-roberta-large
|
navteca
| 2021-03-10T14:57:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:quora",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
datasets:
- quora
language: en
license: mit
pipeline_tag: text-classification
tags:
- roberta
- text-classification
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
This model uses [roberta-large](https://huggingface.co/roberta-large).
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset.
The model will predict a score between 0 and 1: How likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
The trained model can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
print(scores)
```
|
hd10/semeval2020_task11_tc
|
hd10
| 2021-03-09T18:01:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
Technique Classification for https://propaganda.qcri.org/ptc/index.html
|
tennessejoyce/titlewave-t5-small
|
tennessejoyce
| 2021-03-09T04:03:11Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Titlewave: t5-small
This is one of two models used in the Titlewave project. See https://github.com/tennessejoyce/TitleWave for more information.
This model was fine-tuned on a dataset of Stack Overflow posts, with a ConditionalGeneration head that summarizes the body of a question in order to suggest a title.
|
Darkrider/covidbert_mednli
|
Darkrider
| 2021-03-07T15:20:12Z | 4 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
# CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
It is further fine-tuned on both MedNLI datasets available at Physionet.
[ACL-BIONLP 2019](https://physionet.org/content/mednli-bionlp19/1.0.1/)
[MedNLI from MIMIC](https://physionet.org/content/mednli/1.0.0/)
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
|
tiedeman/opus-mt-en-he
|
tiedeman
| 2021-03-04T17:50:20Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"rust",
"marian",
"text2text-generation",
"translation",
"en",
"he",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- en
- he
tags:
- translation
license: apache-2.0
---
### en-he
* source group: English
* target group: Hebrew
* OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md)
* model: transformer
* source language(s): eng
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-10-04.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip)
* test set translations: [opus-2020-10-04.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt)
* test set scores: [opus-2020-10-04.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng.heb | 37.9 | 0.602 |
### System Info:
- hf_name: en-he
- source_languages: eng
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'he']
- src_constituents: ('English', {'eng'})
- tgt_constituents: ('Hebrew', {'heb'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: eng-heb
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt
- src_alpha3: eng
- tgt_alpha3: heb
- chrF2_score: 0.602
- bleu: 37.9
- brevity_penalty: 1.0
- ref_len: 60359.0
- src_name: English
- tgt_name: Hebrew
- train_date: 2020-10-04 00:00:00
- src_alpha2: en
- tgt_alpha2: he
- prefer_old: False
- short_pair: en-he
- helsinki_git_sha: 61fd6908b37d9a7b21cc3e27c1ae1fccedc97561
- transformers_git_sha: d99ed7ad618037ae878f0758157ed0764bd7f935
- port_machine: LM0-400-22516.local
- port_time: 2020-10-15-16:31
|
patrickvonplaten/wav2vec2-base-100h-13K-steps
|
patrickvonplaten
| 2021-03-03T13:11:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
Fine-tuning of `wav2vec2-base` on 100h of Librispeech training data. Results on "clean" data are very similar to the ones of the [official model](https://huggingface.co/facebook/wav2vec2-base-100h). However, the result on "other" is significantly worse - the model seems to have overfitting to the "clean" data.
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
- 2 GPUs Titan RTX
- Total update steps 13000
- Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~1500 seconds
- Adam with linear decaying learning rate with 3000 warmup steps
- dynamic grouping for batch
- fp16
- attention_mask was **not** used during training
Check: https://wandb.ai/patrickvonplaten/huggingface/reports/Project-Dashboard--Vmlldzo1MDI2MTU?accessToken=69z0mrkoxs1msgh71p4nntr9shi6mll8rhtbo6c56yynygw0scp11d8z9o1xd0uk
*Result (WER)* on Librispeech test:
| "clean" | "other" |
|---|---|
| 6.5 | 18.7 |
|
patrickvonplaten/wav2vec2-large-lv60h-100h-2nd-try
|
patrickvonplaten
| 2021-03-03T13:02:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
Fine-tuning of `wav2vec2-large-lv60` on 100h of Librispeech training data. Results are a bit worse than those reported in the Appendix in Table 3 of the original [paper](https://arxiv.org/pdf/2006.11477.pdf).
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
- 2 GPUs Titan RTX
- Total update steps 17500
- Batch size per GPU: 16 corresponding to a *total batch size* of ca. ~750 seconds
- Adam with linear decaying learning rate with 3000 warmup steps
- dynamic padding for batch
- fp16
- attention_mask was used during training
Check: https://wandb.ai/patrickvonplaten/huggingface/reports/Project-Dashboard--Vmlldzo0OTI0OTc?accessToken=8azw8iyxnbiqd4ytxcgm4hbnfh3x1b2c9l2eyfqfzdqw7l0icreljc9qpx0rkl6f
*Result (WER)* on Librispeech test:
| "clean" | "other" |
|---|---|
| 4.0 | 10.3 |
|
hfl/chinese-xlnet-base
|
hfl
| 2021-03-03T01:44:59Z | 330 | 30 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-large-discriminator
|
hfl
| 2021-03-03T01:42:48Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-small-ex-generator
|
hfl
| 2021-03-03T01:39:16Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-small-discriminator
|
hfl
| 2021-03-03T01:39:00Z | 82 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-180g-large-generator
|
hfl
| 2021-03-03T01:27:24Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-180g-base-discriminator
|
hfl
| 2021-03-03T01:26:14Z | 1,185 | 11 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-180g-small-ex-discriminator
|
hfl
| 2021-03-03T01:25:29Z | 4,609 | 7 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
hfl/chinese-electra-180g-small-ex-generator
|
hfl
| 2021-03-03T01:25:06Z | 1 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
flair/upos-multi-fast
|
flair
| 2021-03-02T22:22:55Z | 402 | 5 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs",
"dataset:ontonotes",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- en
- de
- fr
- it
- nl
- pl
- es
- sv
- da
- no
- fi
- cs
datasets:
- ontonotes
widget:
- text: "Ich liebe Berlin, as they say."
---
## Multilingual Universal Part-of-Speech Tagging in Flair (fast model)
This is the fast multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,88** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech)
Predicts universal POS tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
|ADJ | adjective |
| ADP | adposition |
| ADV | adverb |
| AUX | auxiliary |
| CCONJ | coordinating conjunction |
| DET | determiner |
| INTJ | interjection |
| NOUN | noun |
| NUM | numeral |
| PART | particle |
| PRON | pronoun |
| PROPN | proper noun |
| PUNCT | punctuation |
| SCONJ | subordinating conjunction |
| SYM | symbol |
| VERB | verb |
| X | other |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/upos-multi-fast")
# make example sentence
sentence = Sentence("Ich liebe Berlin, as they say. ")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('pos'):
print(entity)
```
This yields the following output:
```
Span [1]: "Ich" [− Labels: PRON (0.9999)]
Span [2]: "liebe" [− Labels: VERB (0.9999)]
Span [3]: "Berlin" [− Labels: PROPN (0.9997)]
Span [4]: "," [− Labels: PUNCT (1.0)]
Span [5]: "as" [− Labels: SCONJ (0.9991)]
Span [6]: "they" [− Labels: PRON (0.9998)]
Span [7]: "say" [− Labels: VERB (0.9998)]
Span [8]: "." [− Labels: PUNCT (1.0)]
```
So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
from flair.embeddings import StackedEmbeddings, FlairEmbeddings
# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
corpus = MultiCorpus([
UD_ENGLISH(in_memory=False),
UD_GERMAN(in_memory=False),
UD_DUTCH(in_memory=False),
UD_FRENCH(in_memory=False),
UD_ITALIAN(in_memory=False),
UD_SPANISH(in_memory=False),
UD_POLISH(in_memory=False),
UD_CZECH(in_memory=False),
UD_DANISH(in_memory=False),
UD_SWEDISH(in_memory=False),
UD_NORWEGIAN(in_memory=False),
UD_FINNISH(in_memory=False),
])
# 2. what tag do we want to predict?
tag_type = 'upos'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# contextual string embeddings, forward
FlairEmbeddings('multi-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('multi-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=False)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/upos-multi-fast',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
flair/ner-dutch
|
flair
| 2021-03-02T22:03:57Z | 316 | 3 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"nl",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: nl
datasets:
- conll2003
widget:
- text: "George Washington ging naar Washington."
---
# Dutch NER in Flair (default model)
This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,58** (CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on Transformer embeddings and LSTM-CRF.
---
# Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-dutch")
# make example sentence
sentence = Sentence("George Washington ging naar Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.997)]
Span [5]: "Washington" [− Labels: LOC (0.9996)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03_DUTCH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03_DUTCH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize embeddings
embeddings = TransformerWordEmbeddings('wietsedv/bert-base-dutch-cased')
# 5. initialize sequence tagger
tagger: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-dutch',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik-etal-2019-flair,
title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
author = "Akbik, Alan and
Bergmann, Tanja and
Blythe, Duncan and
Rasul, Kashif and
Schweter, Stefan and
Vollgraf, Roland",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
year = "2019",
url = "https://www.aclweb.org/anthology/N19-4010",
pages = "54--59",
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
stefan-it/flair-distilbert-ner-germeval14
|
stefan-it
| 2021-03-02T18:32:30Z | 10 | 1 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:germeval_14",
"license:mit",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
datasets:
- germeval_14
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "Hugging Face ist eine französische Firma mit Sitz in New York."
license: mit
---
# Flair NER model trained on GermEval14 dataset
This model was trained on the official [GermEval14](https://sites.google.com/site/germeval2014ner/data)
dataset using the [Flair](https://github.com/flairNLP/flair) framework.
It uses a fine-tuned German DistilBERT model from [here](https://huggingface.co/distilbert-base-german-cased).
# Results
| Dataset \ Run | Run 1 | Run 2 | Run 3† | Run 4 | Run 5 | Avg.
| ------------- | ----- | ----- | --------- | ----- | ----- | ----
| Development | 87.05 | 86.52 | **87.34** | 86.85 | 86.46 | 86.84
| Test | 85.43 | 85.88 | 85.72 | 85.47 | 85.62 | 85.62
† denotes that this model is selected for upload.
# Flair Fine-Tuning
We used the following script to fine-tune the model on the GermEval14 dataset:
```python
from argparse import ArgumentParser
import torch, flair
# dataset, model and embedding imports
from flair.datasets import GERMEVAL_14
from flair.embeddings import TransformerWordEmbeddings
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
if __name__ == "__main__":
# All arguments that can be passed
parser = ArgumentParser()
parser.add_argument("-s", "--seeds", nargs='+', type=int, default='42') # pass list of seeds for experiments
parser.add_argument("-c", "--cuda", type=int, default=0, help="CUDA device") # which cuda device to use
parser.add_argument("-m", "--model", type=str, help="Model name (such as Hugging Face model hub name")
# Parse experimental arguments
args = parser.parse_args()
# use cuda device as passed
flair.device = f'cuda:{str(args.cuda)}'
# for each passed seed, do one experimental run
for seed in args.seeds:
flair.set_seed(seed)
# model
hf_model = args.model
# initialize embeddings
embeddings = TransformerWordEmbeddings(
model=hf_model,
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=False,
respect_document_boundaries=False,
)
# select dataset depending on which language variable is passed
corpus = GERMEVAL_14()
# make the dictionary of tags to predict
tag_dictionary = corpus.make_tag_dictionary('ner')
# init bare-bones sequence tagger (no reprojection, LSTM or CRF)
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# init the model trainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# make string for output folder
output_folder = f"flert-ner-{hf_model}-{seed}"
# train with XLM parameters (AdamW, 20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train(
output_folder,
learning_rate=5.0e-5,
mini_batch_size=16,
mini_batch_chunk_size=1,
max_epochs=10,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
train_with_dev=False,
)
```
|
sarnikowski/convbert-small-da-cased
|
sarnikowski
| 2021-03-01T22:15:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"convbert",
"da",
"arxiv:2008.02496",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: da
license: cc-by-4.0
---
# Danish ConvBERT small (cased)
[ConvBERT](https://arxiv.org/abs/2008.02496) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers
## Usage
```python
from transformers import ConvBertTokenizer, ConvBertModel
tokenizer = ConvBertTokenizer.from_pretrained("sarnikowski/convbert-small-da-cased")
model = ConvBertModel.from_pretrained("sarnikowski/convbert-small-da-cased")
```
## Questions?
If you have any questions feel free to open an issue on the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com
|
nsi319/legal-led-base-16384
|
nsi319
| 2021-03-01T12:33:48Z | 298 | 13 |
transformers
|
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"summarization",
"en",
"license:mit",
"autotrain_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags: summarization
metrics:
- rouge
- precision
inference: false
license: mit
---
## LED for legal summarization of documents
This is a Longformer Encoder Decoder ([led-base-16384](https://huggingface.co/allenai/led-base-16384)) model for the **legal domain**, trained for **long document abstractive summarization** task. The length of the document can be upto 16,384 tokens.
## Training data
The **legal-led-base-16384** model was trained on [sec-litigation-releases](https://www.sec.gov/litigation/litreleases.htm) dataset consisting more than 2700 litigation releases and complaints.
## How to use
```Python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("nsi319/legal-led-base-16384")
model = AutoModelForSeq2SeqLM.from_pretrained("nsi319/legal-led-base-16384")
padding = "max_length"
text="""On March 2, 2018, the Securities and Exchange Commission announced securities fraud charges against a U.K.-based broker-dealer and its investment manager in connection with manipulative trading in the securities of HD View 360 Inc., a U.S.-based microcap issuer. The SEC also announced charges against HD View's CEO, another individual, and three entities they control for manipulating HD View's securities as well as the securities of another microcap issuer, West Coast Ventures Group Corp. The SEC further announced the institution of an order suspending trading in the securities of HD View.These charges arise in part from an undercover operation by the Federal Bureau of Investigation, which also resulted in related criminal prosecutions against these defendants by the Office of the United States Attorney for the Eastern District of New York.In a complaint filed in the U.S. District Court for the Eastern District of New York, the SEC alleges that Beaufort Securities Ltd. and Peter Kyriacou, an investment manager at Beaufort, manipulated the market for HD View's common stock. The scheme involved an undercover FBI agent who described his business as manipulating U.S. stocks through pump-and-dump schemes. Kyriacou and the agent discussed depositing large blocks of microcap stock in Beaufort accounts, driving up the price of the stock through promotions, manipulating the stock's price and volume through matched trades, and then selling the shares for a large profit.The SEC's complaint against Beaufort and Kyriacou alleges that they:opened brokerage accounts for the undercover agent in the names of nominees in order to conceal his identity and his connection to the anticipated trading activity in the accounts suggested that the undercover agent could create the false appearance that HD View's stock was liquid in advance of a pump-and-dump by "gam[ing] the market" through matched trades executed multiple purchase orders of HD View shares with the understanding that Beaufort's client had arranged for an associate to simultaneously offer an equivalent number of shares at the same priceA second complaint filed by the SEC in the U.S. District Court for the Eastern District of New York alleges that in a series of recorded telephone conversations with the undercover agent, HD View CEO Dennis Mancino and William T. Hirschy agreed to manipulate HD View's common stock by using the agent's network of brokers to generate fraudulent retail demand for the stock in exchange for a kickback from the trading proceeds. According to the complaint, the three men agreed that Mancino and Hirschy would manipulate HD View stock to a higher price before using the agent's brokers to liquidate their positions at an artificially inflated price. The SEC's complaint also alleges that Mancino and Hirschy executed a "test trade" on Jan. 31, 2018, coordinated by the agent, consisting of a sell order placed by the defendants filled by an opposing purchase order placed by a broker into an account at Beaufort. Unbeknownst to Mancino and Hirschy, the Beaufort account used for this trade was a nominal account that was opened and funded by the agent. The SEC's complaint also alleges that, prior to their contact with the undercover agent, Mancino and Hirschy manipulated the market for HD View and for West Coast by using brokerage accounts that they owned, controlled, or were associated with –including TJM Investments Inc., DJK Investments 10 Inc., WT Consulting Group LLC – to effect manipulative "matched trades."The SEC's complaint against Beaufort and Kyriacou charges the defendants with violating Section 10(b) of the Securities Exchange Act of 1934 and Rule 10b-5 thereunder. The SEC also charged Hirschy, Mancino, and their corporate entities with violating Section 17(a)(1) of the Securities Act of 1933, Sections 9(a)(1), 9(a)(2), and 10(b) of the Exchange Act and Rules 10b-5(a) and (c) thereunder. The SEC is seeking injunctions, disgorgement, prejudgment interest, penalties, and penny stock bars from Beaufort and Kyriacou. With respect to Hirschy, Mancino, and their corporate entities, the SEC is seeking injunctions, disgorgement, prejudgment interest, penalties, penny stock bars, and an officer-and-director bar against Mancino.The investigation was conducted in the SEC's New York Regional Office by Tejal Shah and Joseph Darragh, Lorraine Collazo, and Michael D. Paley of the Microcap Fraud Task Force and supervised by Lara S. Mehraban, and in Washington, D.C. by Patrick L. Feeney, Robert Nesbitt, and Kevin Guerrero, and supervised by Antonia Chion. Preethi Krishnamurthy and Ms. Shah will lead the SEC's litigation against Beaufort and Kyriacou. Ann H. Petalas and Mr. Feeney, under the supervision of Cheryl Crumpton, will handle the SEC's litigation against Mancino, Hirschy, and their entities. The SEC appreciates the assistance of the Office of the United States Attorney for the Eastern District of New York, the Federal Bureau of Investigation, the Internal Revenue Service, the Alberta Securities Commission, the Ontario Securities Commission, the Financial Conduct Authority of the United Kingdom, and the Financial Industry Regulatory Authority.The Commission's investigation in this matter is continuing."""
input_tokenized = tokenizer.encode(text, return_tensors='pt',padding=padding,pad_to_max_length=True, max_length=6144,truncation=True)
summary_ids = model.generate(input_tokenized,
num_beams=4,
no_repeat_ngram_size=3,
length_penalty=2,
min_length=350,
max_length=500)
summary = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0]
### Summary Output
# On March 2, 2018, the Securities and Exchange Commission charged Beaufort Securities Ltd. and Peter Kyriacou, an investment manager at Beaufort, with manipulating the market for HD View 360 Inc., a U.S.-based microcap issuer. The SEC also announced charges against HD View's CEO, another individual, and three entities they control for manipulating HD View through pump-and-dump schemes. According to the SEC's complaint, the defendants discussed depositing large blocks of microcap stock in Beaufort accounts, driving up the price of the stock through promotions, manipulating the stock's price and volume through matched trades, and then selling the shares for a large profit. In a parallel action, the United States Attorney's Office for the Eastern District of New York announced criminal charges against the defendants. On March 4, the SEC announced the entry of an order suspending trading in the securities of HD View and for West Coast, pending the outcome of a parallel criminal action by the Federal Bureau of Investigation. Following the announcement of the suspension, HD View stock prices and volume increased significantly, and the defendants agreed to pay over $1.5 million in disgorgement, prejudgment interest, penalties, and an officer and director bar. Beaufort agreed to settle the charges without admitting or denying the allegations of the complaint, and to pay a $1 million civil penalty. The SEC's investigation, which is continuing, has been conducted by Patrick McCluskey and Cheryl Crumpton of the SEC Enforcement Division's Market Abuse Unit in the New York Regional Office. The SEC appreciates the assistance of the Financial Industry Regulatory Authority of the United Kingdom, the Canadian Securities Commission, the Alberta Securities Commission and the Ontario Securities Commission.
```
## Evaluation results
When the model is used for summarizing legal documents, it achieves the following results:
| Model | rouge1 | rouge1-precision | rouge2 | rouge2-precision | rougeL | rougeL-precision |
|:-----------:|:-----:|:-----:|:------:|:-----:|:------:|:-----:|
| legal-led-base-16384 | **55.69** | **61.73** | **29.03** | **36.68** | **32.65** | **40.43** |
| led-base-16384 | 29.19 | 30.43 | 15.23 | 16.27 | 16.32 | 16.58 |
|
atahmasb/tf-layoutlm-large-uncased
|
atahmasb
| 2021-02-26T20:33:39Z | 7 | 0 |
transformers
|
[
"transformers",
"tf",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# LayoutLM
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
[LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318)
Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, [KDD 2020](https://www.kdd.org/kdd2020/accepted-papers)
## Training data
We pre-train LayoutLM on IIT-CDIP Test Collection 1.0\* dataset with two settings.
* LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters
* LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters **(This Model)**
## Citation
If you find LayoutLM useful in your research, please cite the following paper:
``` latex
@misc{xu2019layoutlm,
title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou},
year={2019},
eprint={1912.13318},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
flair/ner-danish
|
flair
| 2021-02-26T15:33:02Z | 70 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"da",
"dataset:DaNE",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: da
datasets:
- DaNE
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
---
# Danish NER in Flair (default model)
This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **81.78** (DaNER)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on Transformer embeddings and LSTM-CRF.
---
# Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-danish")
# make example sentence
sentence = Sentence("Jens Peter Hansen kommer fra Danmark")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2,3]: "Jens Peter Hansen" [− Labels: PER (0.9961)]
Span [6]: "Danmark" [− Labels: LOC (0.9816)]
```
So, the entities "*Jens Peter Hansen*" (labeled as a **person**) and "*Danmark*" (labeled as a **location**) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*".
---
### Training: Script to train this model
The model was trained by the [DaNLP project](https://github.com/alexandrainst/danlp) using the [DaNE corpus](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#danish-dependency-treebank-dane-dane). Check their repo for more information.
The following Flair script may be used to train such a model:
```python
from flair.data import Corpus
from flair.datasets import DANE
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = DANE()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('da'),
# contextual string embeddings, forward
FlairEmbeddings('da-forward'),
# contextual string embeddings, backward
FlairEmbeddings('da-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-danish',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following papers when using this model.
```
@inproceedings{akbik-etal-2019-flair,
title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
author = "Akbik, Alan and
Bergmann, Tanja and
Blythe, Duncan and
Rasul, Kashif and
Schweter, Stefan and
Vollgraf, Roland",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
year = "2019",
url = "https://www.aclweb.org/anthology/N19-4010",
pages = "54--59",
}
```
And check the [DaNLP project](https://github.com/alexandrainst/danlp) for more information.
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
valhalla/s2t_librispeech_large
|
valhalla
| 2021-02-26T14:25:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
---
TODO: [To be filled]
## Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset
from transformers import Speech2TextTransformerForConditionalGeneration, Speech2TextTransformerTokenizer
import soundfile as sf
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset
model = Speech2TextTransformerForConditionalGeneration.from_pretrained("valhalla/s2t_librispeech_large").to("cuda")
tokenizer = Speech2TextTransformerTokenizer.from_pretrained("valhalla/s2t_librispeech_large", do_upper_case=True)
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
librispeech_eval = librispeech_eval.map(map_to_array)
def map_to_pred(batch):
features = tokenizer(batch["speech"], sample_rate=16000, padding=True, return_tensors="pt")
input_features = features.input_features.to("cuda")
attention_mask = features.attention_mask.to("cuda")
gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
batch["transcription"] = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 3.3 | 7.5 |
|
valhalla/s2t_librispeech_medium
|
valhalla
| 2021-02-26T14:24:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
---
TODO: [To be filled]
## Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset
from transformers import Speech2TextTransformerForConditionalGeneration, Speech2TextTransformerTokenizer
import soundfile as sf
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset
model = Speech2TextTransformerForConditionalGeneration.from_pretrained("valhalla/s2t_librispeech_medium").to("cuda")
tokenizer = Speech2TextTransformerTokenizer.from_pretrained("valhalla/s2t_librispeech_medium", do_upper_case=True)
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
librispeech_eval = librispeech_eval.map(map_to_array)
def map_to_pred(batch):
features = tokenizer(batch["speech"], sample_rate=16000, padding=True, return_tensors="pt")
input_features = features.input_features.to("cuda")
attention_mask = features.attention_mask.to("cuda")
gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
batch["transcription"] = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 3.5 | 7.8 |
|
sismetanin/xlm_roberta_base-ru-sentiment-rusentiment
|
sismetanin
| 2021-02-25T23:57:49Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## XML-RoBERTa-Base-ru-sentiment-RuSentiment
XML-RoBERTa-Base-ru-sentiment-RuSentiment is a [XML-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@inproceedings{rogers2018rusentiment,
title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
booktitle={Proceedings of the 27th international conference on computational linguistics},
pages={755--763},
year={2018}
}
```
|
sismetanin/xlm_roberta_base-ru-sentiment-rureviews
|
sismetanin
| 2021-02-25T23:51:22Z | 23 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## XLM-RoBERTa-Base-ru-sentiment-RuReviews
XLM-RoBERTa-Base-ru-sentiment-RuReviews is a [XLM-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}
```
|
sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rureviews
|
sismetanin
| 2021-02-25T23:49:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## MBARTRuSumGazeta-ru-sentiment-RuReviews
MBARTRuSumGazeta-ru-sentiment-RuReviews is a [MBARTRuSumGazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}
```
|
voidful/dpr-ctx_encoder-bert-base-multilingual
|
voidful
| 2021-02-21T09:00:44Z | 34 | 6 |
transformers
|
[
"transformers",
"pytorch",
"dpr",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: multilingual
datasets:
- NQ
- Trivia
- SQuAD
- MLQA
- DRCD
---
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
2. [Trivia](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
3. [SQuAD](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
4. [DRCD*](https://github.com/DRCKnowledgeTeam/DRCD)
5. [MLQA*](https://github.com/facebookresearch/MLQA)
`question pairs for train`： 644,217
`question pairs for dev`： 73,710
*DRCD and MLQA are converted using script from haystack [squad_to_dpr.py](https://github.com/deepset-ai/haystack/blob/master/haystack/retriever/squad_to_dpr.py)
## Training Script
I use the script from [haystack](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.ipynb)
## Usage
```python
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
tokenizer = DPRContextEncoderTokenizer.from_pretrained('voidful/dpr-ctx_encoder-bert-base-multilingual')
model = DPRContextEncoder.from_pretrained('voidful/dpr-ctx_encoder-bert-base-multilingual')
input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
embeddings = model(input_ids).pooler_output
```
Follow the tutorial from `haystack`:
[Better Retrievers via "Dense Passage Retrieval"](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)
```
from haystack.retriever.dense import DensePassageRetriever
retriever = DensePassageRetriever(document_store=document_store,
query_embedding_model="voidful/dpr-question_encoder-bert-base-multilingual",
passage_embedding_model="voidful/dpr-ctx_encoder-bert-base-multilingual",
max_seq_len_query=64,
max_seq_len_passage=256,
batch_size=16,
use_gpu=True,
embed_title=True,
use_fast_tokenizers=True)
```
|
superspray/distilbert_base_squad2_custom_dataset
|
superspray
| 2021-02-20T07:33:31Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Distilbert_Base fine-tuned on SQuAD2.0 and custom QA dataset
This model is [twmkn9/distilbert-base-uncased-squad2] trained on additional custom dataset as:
```
!python3 run_squad.py --model_type distilbert \
--model_name_or_path /content/distilbert_base_384 \
--do_lower_case \
--output_dir /content/model/\
--do_train \
--train_file $data_dir/additional_qa.json\
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size 4
```
We used Google Colab for training the model,
|
superspray/electra_large_discriminator_squad2_custom_dataset
|
superspray
| 2021-02-20T07:00:12Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# Question & Answering Model for 'Save Your Minutes' from Dobby-AI
Electra_Large Discriminator fine-tuned on SQuAD2.0 and custom QA dataset
This model is [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512/blob/main/README.md)
trained on additional custom dataset as:
```
!python3 run_squad.py --model_type electra \
--model_name_or_path /content/electra_large_512 \
--do_lower_case \
--output_dir /content/model/\
--do_train \
--train_file $data_dir/additional_qa.json\
--version_2_with_negative \
--do_lower_case \
--num_train_epochs 3 \
--weight_decay 0.01 \
--learning_rate 3e-5 \
--max_grad_norm 0.5 \
--adam_epsilon 1e-6 \
--max_seq_length 512 \
--doc_stride 128 \
--threads 12 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size 4
```
We used Google Colab for training the model,
|
joeddav/distilbert-base-uncased-agnews-student
|
joeddav
| 2021-02-18T20:41:19Z | 14 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"tensorflow",
"en",
"dataset:ag_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- ag_news
license: mit
widget:
- text: "Armed conflict has been a near-constant policial and economic burden."
- text: "Tom Brady won his seventh Super Bowl last night."
- text: "Dow falls more than 100 points after disappointing jobs data"
- text: "A new moon has been discovered in Jupter's orbit."
---
# distilbert-base-uncased-agnews-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using [this
script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
It is the result of the demo notebook
[here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing), where more details
about the model can be found.
- Teacher model: [roberta-large-mnli](https://huggingface.co/roberta-large-mnli)
- Teacher hypothesis template: `"This text is about {}."`
## Intended Usage
The model can be used like any other model trained on AG's News, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student.
|
julien-c/timm-dpn92
|
julien-c
| 2021-02-18T11:18:56Z | 2 | 0 |
timm
|
[
"timm",
"pytorch",
"image-classification",
"dpn",
"dataset:imagenet",
"arxiv:1707.01629",
"arxiv:1906.02659",
"arxiv:2010.15052",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- timm
- dpn
license: apache-2.0
datasets:
- imagenet
---
# `dpn92` from `rwightman/pytorch-image-models`
From [`rwightman/pytorch-image-models`](https://github.com/rwightman/pytorch-image-models):
```
""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
This implementation is compatible with the pretrained weights from cypw's MXNet implementation.
Hacked together by / Copyright 2020 Ross Wightman
"""
```
## Model description
[Dual Path Networks](https://arxiv.org/abs/1707.01629)
## Intended uses & limitations
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).
### How to use
You can use this model with the usual factory method in `timm`:
```python
import PIL
import timm
import torch
model = timm.create_model("julien-c/timm-dpn92")
img = PIL.Image.open(path_to_an_image)
img = img.convert("RGB")
config = model.default_cfg
if isinstance(config["input_size"], tuple):
img_size = config["input_size"][-2:]
else:
img_size = config["input_size"]
transform = timm.data.transforms_factory.transforms_imagenet_eval(
img_size=img_size,
interpolation=config["interpolation"],
mean=config["mean"],
std=config["std"],
)
input_tensor = transform(cat_img)
input_tensor = input_tensor.unsqueeze(0)
# ^ batch size = 1
with torch.no_grad():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
### Limitations and bias
The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will
probably not generalize well on drawings or images containing multiple objects with different labels.
The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or
models created by fine-tuning this model will work better on images picturing scenes from these countries (see
[this paper](https://arxiv.org/abs/1906.02659) for examples).
More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an
unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in
the training images.
## Training data
This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of
hand-annotated images with 1,000 categories.
## Training procedure
To be completed
### Preprocessing
To be completed
## Evaluation results
To be completed
### BibTeX entry and citation info
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
```
and
```bibtex
@misc{chen2017dual,
title={Dual Path Networks},
author={Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin and Shuicheng Yan and Jiashi Feng},
year={2017},
eprint={1707.01629},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
flexudy/t5-small-wav2vec2-grammar-fixer
|
flexudy
| 2021-02-16T01:56:40Z | 131,235 | 12 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# flexudy-pipe-question-generation-v2
After transcribing your audio with Wav2Vec2, you might be interested in a post processor.
All paragraphs had at most 128 tokens (separated by white spaces)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = "flexudy/t5-small-wav2vec2-grammar-fixer"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
sent = """GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS"""
input_text = "fix: { " + sent + " } </s>"
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, truncation=True, add_special_tokens=True)
outputs = model.generate(
input_ids=input_ids,
max_length=256,
num_beams=4,
repetition_penalty=1.0,
length_penalty=1.0,
early_stopping=True
)
sentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(f"{sentence}")
```
INPUT 1:
```
WHEN ARE YOU COMING TOMORROW I AM ASKING BECAUSE OF THE MONEY YOU OWE ME PLEASE GIVE IT TO ME I AM WAITING YOU HAVE BEEN AVOIDING ME SINCE TWO THOUSAND AND THREE
```
OUTPUT 1:
```
When are you coming tomorrow? I am asking because of the money you owe me, please give it to me. I am waiting. You have been avoiding me since 2003.
```
INPUT 2:
```
GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS
```
OUTPUT 2:
```
Going along Slushy Country Roads and speaking to Damp audiences in Draughty School rooms day after day for a fortnight, he'll have to put in an appearance at some place of worship on Sunday morning and he can come to us immediately afterwards.
```
I strongly recommend improving the performance via further fine-tuning or by training more examples.
- Possible Quick Rule based improvements: Align the transcribed version and the generated version. If the similarity of two words (case-insensitive) vary by more than some threshold based on some similarity metric (e.g. Levenshtein), then keep the transcribed word.
|
CouchCat/ma_sa_v7_distil
|
CouchCat
| 2021-02-15T23:19:57Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"sentiment-analysis",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language: en
license: mit
tags:
- sentiment-analysis
widget:
- text: "I am disappointed in the terrible quality of my dress"
---
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive
### Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil")
model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil")
```
|
tner/xlm-roberta-large-uncased-mit-movie-trivia
|
tner
| 2021-02-13T00:11:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-movie-trivia")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-movie-trivia")
```
|
tner/xlm-roberta-large-panx-dataset-ru
|
tner
| 2021-02-13T00:11:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ru")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ru")
```
|
tner/xlm-roberta-large-panx-dataset-ja
|
tner
| 2021-02-13T00:11:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ja")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-ja")
```
|
tner/xlm-roberta-large-conll2003
|
tner
| 2021-02-13T00:11:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-conll2003")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-conll2003")
```
|
tner/xlm-roberta-base-uncased-panx-dataset-en
|
tner
| 2021-02-13T00:10:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-panx-dataset-en")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-panx-dataset-en")
```
|
tner/xlm-roberta-base-panx-dataset-ru
|
tner
| 2021-02-13T00:08:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ru")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-ru")
```
|
asahi417/tner-xlm-roberta-base-uncased-ontonotes5
|
asahi417
| 2021-02-13T00:08:01Z | 287 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-ontonotes5")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-ontonotes5")
```
|
tner/xlm-roberta-base-panx-dataset-en
|
tner
| 2021-02-13T00:07:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-en")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-en")
```
|
tner/xlm-roberta-large-uncased-mit-restaurant
|
tner
| 2021-02-13T00:06:06Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-restaurant")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-mit-restaurant")
```
|
tner/xlm-roberta-large-panx-dataset-es
|
tner
| 2021-02-13T00:04:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-es")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-panx-dataset-es")
```
|
tner/xlm-roberta-large-fin
|
tner
| 2021-02-13T00:04:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-fin")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-fin")
```
|
asahi417/tner-xlm-roberta-large-all-english
|
asahi417
| 2021-02-12T23:48:50Z | 6,359 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-all-english")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-large-all-english")
```
|
tner/xlm-roberta-base-uncased-wnut2017
|
tner
| 2021-02-12T23:48:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-wnut2017")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-wnut2017")
```
|
tner/xlm-roberta-base-uncased-fin
|
tner
| 2021-02-12T23:47:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-fin")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-fin")
```
|
tner/xlm-roberta-base-uncased-bionlp2004
|
tner
| 2021-02-12T23:35:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-bionlp2004")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-bionlp2004")
```
|
tner/xlm-roberta-base-uncased-all-english
|
tner
| 2021-02-12T23:35:06Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-all-english")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-uncased-all-english")
```
|
tner/xlm-roberta-base-panx-dataset-es
|
tner
| 2021-02-12T23:34:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
# XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-es")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-panx-dataset-es")
```
|
Musixmatch/umberto-commoncrawl-cased-v1
|
Musixmatch
| 2021-02-12T11:31:59Z | 16,559 | 14 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: it
---
# UmBERTo Commoncrawl Cased
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1)
<p align="center">
<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
</p>
## Dataset
UmBERTo-Commoncrawl-Cased utilizes the Italian subcorpus of [OSCAR](https://traces1.inria.fr/oscar/) as training set of the language model. We used deduplicated version of the Italian corpus that consists in 70 GB of plain text data, 210M sentences with 11B words where the sentences have been filtered and shuffled at line level in order to be used for NLP research.
## Pre-trained model
| Model | WWM | Cased | Tokenizer | Vocab Size | Train Steps | Download |
| ------ | ------ | ------ | ------ | ------ |------ | ------ |
| `umberto-commoncrawl-cased-v1` | YES | YES | SPM | 32K | 125k | [Link](http://bit.ly/35zO7GH) |
This model was trained with [SentencePiece](https://github.com/google/sentencepiece) and Whole Word Masking.
## Downstream Tasks
These results refers to umberto-commoncrawl-cased model. All details are at [Umberto](https://github.com/musixmatchresearch/umberto) Official Page.
#### Named Entity Recognition (NER)
| Dataset | F1 | Precision | Recall | Accuracy |
| ------ | ------ | ------ | ------ | ------ |
| **ICAB-EvalITA07** | **87.565** | 86.596 | 88.556 | 98.690 |
| **WikiNER-ITA** | **92.531** | 92.509 | 92.553 | 99.136 |
#### Part of Speech (POS)
| Dataset | F1 | Precision | Recall | Accuracy |
| ------ | ------ | ------ | ------ | ------ |
| **UD_Italian-ISDT** | 98.870 | 98.861 | 98.879 | **98.977** |
| **UD_Italian-ParTUT** | 98.786 | 98.812 | 98.760 | **98.903** |
## Usage
##### Load UmBERTo with AutoModel, Autotokenizer:
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
umberto = AutoModel.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
encoded_input = tokenizer.encode("Umberto Eco è stato un grande scrittore")
input_ids = torch.tensor(encoded_input).unsqueeze(0) # Batch size 1
outputs = umberto(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output
```
##### Predict masked token:
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="Musixmatch/umberto-commoncrawl-cased-v1",
tokenizer="Musixmatch/umberto-commoncrawl-cased-v1"
)
result = fill_mask("Umberto Eco è <mask> un grande scrittore")
# {'sequence': '<s> Umberto Eco è considerato un grande scrittore</s>', 'score': 0.18599839508533478, 'token': 5032}
# {'sequence': '<s> Umberto Eco è stato un grande scrittore</s>', 'score': 0.17816807329654694, 'token': 471}
# {'sequence': '<s> Umberto Eco è sicuramente un grande scrittore</s>', 'score': 0.16565583646297455, 'token': 2654}
# {'sequence': '<s> Umberto Eco è indubbiamente un grande scrittore</s>', 'score': 0.0932890921831131, 'token': 17908}
# {'sequence': '<s> Umberto Eco è certamente un grande scrittore</s>', 'score': 0.054701317101716995, 'token': 5269}
```
## Citation
All of the original datasets are publicly available or were released with the owners' grant. The datasets are all released under a CC0 or CCBY license.
* UD Italian-ISDT Dataset [Github](https://github.com/UniversalDependencies/UD_Italian-ISDT)
* UD Italian-ParTUT Dataset [Github](https://github.com/UniversalDependencies/UD_Italian-ParTUT)
* I-CAB (Italian Content Annotation Bank), EvalITA [Page](http://www.evalita.it/)
* WIKINER [Page](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) , [Paper](https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub)
```
@inproceedings {magnini2006annotazione,
title = {Annotazione di contenuti concettuali in un corpus italiano: I - CAB},
author = {Magnini,Bernardo and Cappelli,Amedeo and Pianta,Emanuele and Speranza,Manuela and Bartalesi Lenzi,V and Sprugnoli,Rachele and Romano,Lorenza and Girardi,Christian and Negri,Matteo},
booktitle = {Proc.of SILFI 2006},
year = {2006}
}
@inproceedings {magnini2006cab,
title = {I - CAB: the Italian Content Annotation Bank.},
author = {Magnini,Bernardo and Pianta,Emanuele and Girardi,Christian and Negri,Matteo and Romano,Lorenza and Speranza,Manuela and Lenzi,Valentina Bartalesi and Sprugnoli,Rachele},
booktitle = {LREC},
pages = {963--968},
year = {2006},
organization = {Citeseer}
}
```
## Authors
**Loreto Parisi**: `loreto at musixmatch dot com`, [loretoparisi](https://github.com/loretoparisi)
**Simone Francia**: `simone.francia at musixmatch dot com`, [simonefrancia](https://github.com/simonefrancia)
**Paolo Magnani**: `paul.magnani95 at gmail dot com`, [paulthemagno](https://github.com/paulthemagno)
## About Musixmatch AI
We do Machine Learning and Artificial Intelligence @[musixmatch](https://twitter.com/Musixmatch)
Follow us on [Twitter](https://twitter.com/musixmatchai) [Github](https://github.com/musixmatchresearch)
|
microsoft/deberta-xxlarge-v2
|
microsoft
| 2021-02-11T02:05:17Z | 155 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)
|
microsoft/deberta-xlarge-v2
|
microsoft
| 2021-02-11T02:04:50Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)
|
microsoft/deberta-xlarge-v2-mnli
|
microsoft
| 2021-02-11T02:04:40Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli)
|
valhalla/longformer-base-4096-finetuned-squadv1
|
valhalla
| 2021-02-10T16:35:40Z | 513 | 22 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"rust",
"longformer",
"question-answering",
"dataset:squad_v1",
"arxiv:2004.05150",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
datasets:
- squad_v1
license: mit
---
# LONGFORMER-BASE-4096 fine-tuned on SQuAD v1
This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task.
[Longformer](https://arxiv.org/abs/2004.05150) model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it
> `Longformer` is a BERT-like model for long documents.
The pre-trained model can handle sequences with upto 4096 tokens.
## Model Training
This model was trained on google colab v100 GPU. You can find the fine-tuning colab here [](https://colab.research.google.com/drive/1zEl5D-DdkBKva-DdreVOmN0hrAfzKG1o?usp=sharing).
Few things to keep in mind while training longformer for QA task,
by default longformer uses sliding-window local attention on all tokens. But For QA, all question tokens should have global attention. For more details on this please refer the paper. The `LongformerForQuestionAnswering` model automatically does that for you. To allow it to do that
1. The input sequence must have three sep tokens, i.e the sequence should be encoded like this
` <s> question</s></s> context</s>`. If you encode the question and answer as a input pair, then the tokenizer already takes care of that, you shouldn't worry about it.
2. `input_ids` should always be a batch of examples.
## Results
|Metric | # Value |
|-------------|---------|
| Exact Match | 85.1466 |
| F1 | 91.5415 |
## Model in Action 🚀
```python
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering,
tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
question = "What has Huggingface done ?"
encoding = tokenizer(question, text, return_tensors="pt")
input_ids = encoding["input_ids"]
# default is local attention everywhere
# the forward method will automatically set global attention on question tokens
attention_mask = encoding["attention_mask"]
start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# output => democratized NLP
```
The `LongformerForQuestionAnswering` isn't yet supported in `pipeline` . I'll update this card once the support has been added.
> Created with ❤️ by Suraj Patil [](https://github.com/patil-suraj/)
[](https://twitter.com/psuraj28)
|
dbernsohn/t5_numbers_gcd
|
dbernsohn
| 2021-02-08T06:52:18Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# numbers_gcd
---
language: en
datasets:
- numbers_gcd
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/numbers_gcd](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetnumbers_gcd) for solving **greatest common divisor** mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/t5_numbers_gcd")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/t5_numbers_gcd")
```
You can then use this model to solve algebra 1d equations into numbers.
```python
query = "What is the highest common factor of 4210884 and 72?"
input_text = f"{query} </s>"
features = tokenizer([input_text], return_tensors='pt')
model.to('cuda')
output = model.generate(input_ids=features['input_ids'].cuda(),
attention_mask=features['attention_mask'].cuda())
tokenizer.decode(output[0])
# <pad> 36</s>
```
Another examples:
+ Calculate the greatest common factor of 3470 and 97090.
+ Answer: 10 Pred: 10
----
+ Calculate the highest common factor of 3480 and 775431.
+ Answer: 87 Pred: 87
----
+ What is the highest common divisor of 26 and 88049?
+ Answer: 13 Pred: 13
----
+ Calculate the highest common factor of 1416 and 24203688.
+ Answer: 1416 Pred: 1416
----
+ Calculate the highest common divisor of 124 and 69445828.
+ Answer: 124 Pred: 124
----
+ What is the greatest common factor of 657906 and 470?
+ Answer: 94 Pred: 94
----
+ What is the highest common factor of 4210884 and 72?
+ Answer: 36 Pred: 36
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/MathLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
dbernsohn/algebra_linear_1d
|
dbernsohn
| 2021-02-03T07:09:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# algebra_linear_1d
---
language: en
datasets:
- algebra_linear_1d
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/algebra_linear_1d](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetalgebra_linear_1d_default_config) for solving **algebra 1d equations** mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/algebra_linear_1d")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/algebra_linear_1d")
```
You can then use this model to solve algebra 1d equations into numbers.
```python
query = "Solve 0 = 1026*x - 2474 + 46592 for x"
input_text = f"{query} </s>"
features = tokenizer([input_text], return_tensors='pt')
model.to('cuda')
output = model.generate(input_ids=features['input_ids'].cuda(),
attention_mask=features['attention_mask'].cuda())
tokenizer.decode(output[0])
# <pad> -41</s>
```
Another examples:
+ Solve 1112*r + 1418*r - 5220 = 587*r - 28536 for r.
+ Answer: -12 Pred: -12
----
+ Solve -119*k + 6*k - 117 - 352 = 322 for k.
+ Answer: -7 Pred: -7
----
+ Solve -547 = -62*t + 437 - 798 for t.
+ Answer: 3 Pred: 3
----
+ Solve 3*j - 3*j + 0*j - 4802 = 98*j for j.
+ Answer: -49 Pred: -49
----
+ Solve 3047*n - 6130*n - 1700 = -3049*n for n.
+ Answer: -50 Pred: -50
----
+ Solve 121*i + 1690 = 76*i - 128*i + 133 for i.
+ Answer: -9 Pred: -9
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/MathLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
ordinarykids/borges02
|
ordinarykids
| 2021-01-29T12:54:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# MyModelName
Borges02
## Model description
You can generate new short stories from Jorge Luis Borges.
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
```
|
ml6team/mt5-small-german-finetune-mlsum
|
ml6team
| 2021-01-28T13:15:00Z | 546 | 9 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"summarization",
"de",
"dataset:mlsum",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: de
tags:
- summarization
datasets:
- mlsum
---
# mT5-small fine-tuned on German MLSUM
This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens.
It was fine-tuned on all German articles present in the train split of the [MLSUM dataset](https://huggingface.co/datasets/mlsum) having less than 384 "words" after splitting on whitespace, which resulted in 80249 articles.
The exact expression to filter the dataset was the following:
```python
dataset = dataset.filter(lambda e: len(e['text'].split()) < 384)
```
## Evaluation results
The fine-tuned model was evaluated on 2000 random articles from the validation set.
Mean [f1 ROUGE scores](https://github.com/pltrdy/rouge) were calculated for both the fine-tuned model and the lead-3 baseline (which simply produces the leading three sentences of the document) and are presented in the following table.
| Model | Rouge-1 | Rouge-2 | Rouge-L |
| ------------- |:-------:| --------:| -------:|
| mt5-small | 0.399 | 0.318 | 0.392 |
| lead-3 | 0.343 | 0.263 | 0.341 |
|
mrm8488/mbart-large-finetuned-opus-it-en-translation
|
mrm8488
| 2021-01-27T13:19:19Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"it",
"en",
"dataset:opus100",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
tags:
- translation
language:
- it
- en
datasets:
- opus100
---
### mbart-large-it-en
This is mbart-large-cc25, finetuned on opus100 for Italian to English translation.
It scores BLEU **25.82** on test set.
|
acul3/mt5-translate-en-id
|
acul3
| 2021-01-25T12:40:58Z | 10 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"id",
"dataset:OPUS",
"dataset:CC-aligned",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
tags:
- translation
language: "id"
license: "mit"
datasets:
- OPUS
- CC-aligned
widget:
- text: "I love you"
---
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligned (en-id sentence pair)
|
hfl/chinese-legal-electra-small-discriminator
|
hfl
| 2021-01-22T05:19:55Z | 1 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
kykim/funnel-kor-base
|
kykim
| 2021-01-22T01:56:37Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"ko",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: ko
---
# Funnel-transformer base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("kykim/funnel-kor-base")
model = FunnelModel.from_pretrained("kykim/funnel-kor-base")
```
|
ray1379/bio-convbert-medium-samll
|
ray1379
| 2021-01-21T02:55:31Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
pretrained convbert_medium-small with PubMed text.
|
typeform/distilroberta-base
|
typeform
| 2021-01-20T14:23:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
datasets:
- openwebtext
---
# DistilRoBERTa base model
Forked from https://huggingface.co/distilroberta-base
|
dbernsohn/t5_wikisql_en2SQL
|
dbernsohn
| 2021-01-18T14:24:37Z | 8 | 5 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# t5_wikisql_en2SQL
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **English** to **SQL** **translation** text2text mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/t5_wikisql_en2SQL")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/t5_wikisql_en2SQL")
```
You can then use this model to translate SQL queries into plain english.
```python
query = "what are the names of all the people in the USA?"
input_text = f"translate English to Sql: {query} </s>"
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'].cuda(),
attention_mask=features['attention_mask'].cuda())
tokenizer.decode(output[0])
# Output: "SELECT Name FROM table WHERE Country = USA"
```
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/SQLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
subham92/translation_model_by_subham
|
subham92
| 2021-01-18T10:29:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"fi",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- fi
- en
tags:
- translation
license: apache-2.0
---
|
julien-c/mini_an4_asr_train_raw_bpe_valid
|
julien-c
| 2021-01-12T20:20:17Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3957940#.X90XNelKjkM
This model was trained by kamo-naoyuki using mini_an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
kaesve/SciBERT_patent_reference_extraction
|
kaesve
| 2021-01-12T14:59:37Z | 2 | 1 |
transformers
|
[
"transformers",
"pytorch",
"arxiv:2101.01039",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Reference extraction in patents
This repository contains a finetuned SciBERT model that can extract references to scientific literature from patents.
See https://github.com/kaesve/patent-citation-extraction and https://arxiv.org/abs/2101.01039 for more information.
|
Wikidepia/albert-bahasa-uncased-squad
|
Wikidepia
| 2021-01-11T01:39:05Z | 723 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: id
inference: false
---
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/albert-bahasa-uncased-squad'
)
nlp = pipeline('question-answering', model="Wikidepia/albert-bahasa-uncased-squad", tokenizer=tokenizer)
QA_input = {
'question': 'Kapan orang Normandia berada di Normandia?',
'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) adalah orang-orang yang pada abad ke-10 dan ke-11 memberikan nama mereka ke Normandia, sebuah wilayah di Prancis. Mereka adalah keturunan dari Norse (\ "Norman \" berasal dari \ "Norseman \") perampok dan perompak dari Denmark, Islandia dan Norwegia yang, di bawah pemimpin mereka Rollo, setuju untuk bersumpah setia kepada Raja Charles III dari Francia Barat. Melalui generasi asimilasi dan pencampuran dengan penduduk asli Franka dan Romawi-Gaul, keturunan mereka secara bertahap akan bergabung dengan budaya Francia Barat yang berbasis di Karoling. Identitas budaya dan etnis orang Normandia yang berbeda awalnya muncul pada paruh pertama abad ke-10, dan terus berkembang selama abad-abad berikutnya.'
}
res = nlp(QA_input)
print(res)
```
|
thilina/mt5-sinhalese-english
|
thilina
| 2021-01-03T21:14:26Z | 65 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"mt5",
"text2text-generation",
"translation",
"si",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- si
- en
tags:
- translation
license: apache-2.0
metrics:
- sacrebleu
---
# mt5-sinhalese-english
## Model description
An mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa.
## Training details
- English - Sinhala dataset from the Tatoeba Challenge [Datasets](https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/Data.md)
- [mT5-base pre-trained weights](https://huggingface.co/google/mt5-base)
## Eval results
SacreBLEU score:
- English to Sinhalese: 10.3
- Sinhalese to English: 24.4
|
ShenSeanchen/NLP
|
ShenSeanchen
| 2020-12-29T15:59:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
This is a repo with gather thoughts and experiments on the state-of-the-art techniques in NLP.
|
julien-c/kan-bayashi-jsut_tts_train_tacotron2
|
julien-c
| 2020-12-27T18:48:06Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
inference: false
---
## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381098/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Training
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train
|
julien-c
| 2020-12-27T18:47:01Z | 14 | 2 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
widget:
- text: "Hello, how are you doing?"
---
## Example ESPnet2 TTS model
### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/3989498#.X90RlOlKjkM
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Training config
See full config in [`config.yaml`](./config.yaml)
```yaml
config: conf/tuning/train_tacotron2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_tacotron2_raw
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
monologg/koelectra-small-discriminator
|
monologg
| 2020-12-26T16:23:23Z | 168 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ko
---
# KoELECTRA (Small Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-small-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, ElectraTokenizer
>>> model = ElectraModel.from_pretrained("monologg/koelectra-small-discriminator")
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-small-discriminator")
```
### Tokenizer example
```python
>>> from transformers import ElectraTokenizer
>>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-small-discriminator")
>>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]")
['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]']
>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'E', '##L', '##EC', '##T', '##RA', '##를', '공유', '##합니다', '.', '[SEP]'])
[2, 18429, 41, 6240, 15229, 6204, 20894, 5689, 12622, 10690, 18, 3]
```
## Example using ElectraForPreTraining
```python
import torch
from transformers import ElectraForPreTraining, ElectraTokenizer
discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-small-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-small-discriminator")
sentence = "나는 방금 밥을 먹었다."
fake_sentence = "나는 내일 밥을 먹었다."
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
print(list(zip(fake_tokens, predictions.tolist()[1:-1])))
```
|
m3hrdadfi/albert-fa-base-v2-sentiment-digikala
|
m3hrdadfi
| 2020-12-26T08:48:33Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### Digikala
Digikala user comments provided by [Open Data Mining Program (ODMP)](https://www.digikala.com/opendata/). This dataset contains 62,321 user comments with three labels:
| Label | # |
|:---------------:|:------:|
| no_idea | 10394 |
| not_recommended | 15885 |
| recommended | 36042 |
**Download**
You can download the dataset from [here](https://www.digikala.com/opendata/)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:|
| Digikala User Comments | 81.12 | 81.74 | 80.74 | - |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
m3hrdadfi/albert-fa-base-v2-sentiment-binary
|
m3hrdadfi
| 2020-12-26T08:46:58Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
## Results
The model obtained an F1 score of 87.56% for a composition of all three datasets into a binary-labels `Negative` and `Positive`.
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-multi
|
m3hrdadfi
| 2020-12-26T08:42:15Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### DeepSentiPers
which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.
**Binary:**
1. Negative (Furious + Angry)
2. Positive (Happy + Delighted)
**Multi**
1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted
| Label | # |
|:---------:|:----:|
| Furious | 236 |
| Angry | 1357 |
| Neutral | 2874 |
| Happy | 2848 |
| Delighted | 2516 |
**Download**
You can download the dataset from:
- [SentiPers](https://github.com/phosseini/sentipers)
- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:|
| SentiPers (Multi Class) | 66.12 | 71.11 | - | 69.33 |
| SentiPers (Binary Class) | 91.09 | 92.13 | - | 91.98 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-binary
|
m3hrdadfi
| 2020-12-26T08:42:08Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### DeepSentiPers
which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.
**Binary:**
1. Negative (Furious + Angry)
2. Positive (Happy + Delighted)
**Multi**
1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted
| Label | # |
|:---------:|:----:|
| Furious | 236 |
| Angry | 1357 |
| Neutral | 2874 |
| Happy | 2848 |
| Delighted | 2516 |
**Download**
You can download the dataset from:
- [SentiPers](https://github.com/phosseini/sentipers)
- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:|
| SentiPers (Multi Class) | 66.12 | 71.11 | - | 69.33 |
| SentiPers (Binary Class) | 91.09 | 92.13 | - | 91.98 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
m3hrdadfi/albert-fa-base-v2-ner-peyma
|
m3hrdadfi
| 2020-12-26T08:36:20Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian NER [ARMAN, PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:-----------------:|:-----------:|:-----:|:----------:|:------------:|:--------:|:--------------:|:----------:|
| PEYMA | 88.99 | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
m3hrdadfi/albert-fa-base-v2-clf-digimag
|
m3hrdadfi
| 2020-12-26T08:28:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### DigiMag
A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes.
1. Video Games
2. Shopping Guide
3. Health Beauty
4. Science Technology
5. General
6. Art Cinema
7. Books Literature
| Label | # |
|:------------------:|:----:|
| Video Games | 1967 |
| Shopping Guide | 125 |
| Health Beauty | 1610 |
| Science Technology | 2772 |
| General | 120 |
| Art Cinema | 1667 |
| Books Literature | 254 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1YgrCYY-Z0h2z0-PfWVfOGt1Tv0JDI-qz)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT |
|:-----------------:|:-----------------:|:-----------:|:-----:|
| Digikala Magazine | 92.33 | 93.59 | 90.72 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo.
|
julien-c/voice-activity-detection
|
julien-c
| 2020-12-21T22:38:05Z | 4 | 16 | null |
[
"pytorch",
"pyannote",
"audio",
"voice-activity-detection",
"dataset:dihard",
"arxiv:1910.10655",
"license:mit",
"region:us"
] |
voice-activity-detection
| 2022-03-02T23:29:05Z |
---
tags:
- pyannote
- audio
- voice-activity-detection
datasets:
- dihard
license: mit
inference: false
---
## Example pyannote-audio Voice Activity Detection model
### `pyannote.audio.models.segmentation.PyanNet`
♻️ Imported from https://github.com/pyannote/pyannote-audio-hub
This model was trained by @hbredin.
### Demo: How to use in pyannote-audio
```python
from pyannote.audio.core.inference import Inference
model = Inference('julien-c/voice-activity-detection', device='cuda')
model({
"audio": "TheBigBangTheory.wav"
})
```
### Citing pyannote-audio
```BibTex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
or
```bibtex
@inproceedings{Lavechin2020,
author = {Marvin Lavechin and Marie-Philippe Gill and Ruben Bousbib and Herv\'{e} Bredin and Leibny Paola Garcia-Perera},
title = {{End-to-end Domain-Adversarial Voice Activity Detection}},
year = {2020},
url = {https://arxiv.org/abs/1910.10655},
}
```
|
laboro-ai/distilbert-base-japanese-finetuned-ddqa
|
laboro-ai
| 2020-12-18T03:10:13Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"ja",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: ja
tags:
- distilbert
license: cc-by-nc-4.0
---
|
laboro-ai/distilbert-base-japanese
|
laboro-ai
| 2020-12-18T03:09:19Z | 9,271 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"ja",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ja
tags:
- distilbert
license: cc-by-nc-4.0
---
|
lysandre/tapas-temporary-repo
|
lysandre
| 2020-12-17T15:56:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tapas",
"table-question-answering",
"en",
"dataset:sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
table-question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- sqa
---
# TAPAS base model fine-tuned on Sequential Question Answering (SQA)
This model has 4 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) versions which can be used are:
- `revision="v3"`, which corresponds to `tapas_sqa_inter_masklm_base` (intermediate pre-training, absolute position embeddings)
- `revision="V2"`, which corresponds to `tapas_sqa_masklm_base_reset` (no intermediate pre-training, relative position embeddings)
- `revision="v1"`, which corresponds to `tapas_sqa_masklm_base` (no intermediate pre-training, absolute position embeddings)
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head on top of the pre-trained model, and then jointly
train this randomly initialized classification head with the base model on SQA.
## Intended uses & limitations
You can use this model for answering questions related to a table in a conversational set-up.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Question [SEP] Flattened table [SEP]
```
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128.
In this setup, fine-tuning takes around 20 hours. The optimizer used is Adam with a learning rate of 1.25e-5, and a warmup ratio
of 0.2. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the
`select_one_column` parameter of `TapasConfig`. See also table 12 of the [original paper](https://arxiv.org/abs/2004.02349).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@InProceedings{iyyer2017search-based,
author = {Iyyer, Mohit and Yih, Scott Wen-tau and Chang, Ming-Wei},
title = {Search-based Neural Structured Learning for Sequential Question Answering},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
year = {2017},
month = {July},
abstract = {Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.},
publisher = {Association for Computational Linguistics},
url = {https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/},
}
```
|
jnz/electra-ka
|
jnz
| 2020-12-12T21:53:36Z | 46 | 2 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
### electra-ka is first of its kind, Transformer based, open source Georgian language model.
The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive.
|
flexudy/t5-base-multi-sentence-doctor
|
flexudy
| 2020-12-11T23:33:25Z | 47 | 45 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Sentence-Doctor
Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.
## 1. Problem:
Many NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and **Sentence Boundary Detection**
As a consequence errors caused by these tasks in your NLP pipeline can affect the quality of models in applications. Especially since models are often trained on **clean** input.
## 2. Solution:
Here we provide a model that **attempts** to reconstruct sentences based on the its context (sourrounding text). The task is pretty straightforward:
* `Given an "erroneous" sentence, and its context, reconstruct the "intended" sentence`.
## 3. Use Cases:
* Attempt to repair noisy sentences that where extracted with OCR software or text extractors.
* Attempt to repair sentence boundaries.
* Example (in German): **Input: "und ich bin im**",
* Prefix_Context: "Hallo! Mein Name ist John", Postfix_Context: "Januar 1990 geboren."
* Output: "John und ich bin im Jahr 1990 geboren"
* Possibly sentence level spelling correction -- Although this is not the intended use.
* Input: "I went to church **las yesteday**" => Output: "I went to church last Sunday".
## 4. Disclaimer
Note how we always emphises on the word *attempt*. The current version of the model was only trained on **150K** sentences from the tatoeba dataset: https://tatoeba.org/eng. (50K per language -- En, Fr, De).
Hence, we strongly encourage you to finetune the model on your dataset. We might release a version trained on more data.
## 5. Datasets
We generated synthetic data from the tatoeba dataset: https://tatoeba.org/eng. Randomly applying different transformations on words and characters based on some probabilities. The datasets are available in the data folder (where **sentence_doctor_dataset_300K** is a larger dataset with 100K sentences for each language).
## 6. Usage
### 6.1 Preprocessing
* Let us assume we have the following text (Note that there are no punctuation marks in the text):
```python
text = "That is my job I am a medical doctor I save lives"
```
* You decided extract the sentences and for some obscure reason, you obtained these sentences:
```python
sentences = ["That is my job I a", "m a medical doct", "I save lives"]
```
* You now wish to correct the sentence **"m a medical doct"**.
Here is the single preprocessing step for the model:
```python
input_text = "repair_sentence: " + sentences[1] + " context: {" + sentences[0] + "}{" + sentences[2] + "} </s>"
```
**Explanation**:</br>
* We are telling the model to repair the sentence with the prefix "repair_sentence: "
* Then append the sentence we want to repair **sentence[1]** which is "m a medical doct"
* Next we give some context to the model. In the case, the context is some text that occured before the sentence and some text that appeard after the sentence in the original text.
* To do that, we append the keyword "context :"
* Append **{sentence[0]}** "{That is my job I a}". (Note how it is sourrounded by curly braces).
* Append **{sentence[2]}** "{I save lives}".
* At last we tell the model this is the end of the input with </s>.
```python
print(input_text) # repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>
```
<br/>
**The context is optional**, so the input could also be ```repair_sentence: m a medical doct context: {}{} </s>```
### 6.2 Inference
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
model = AutoModelWithLMHead.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
input_text = "repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(input_ids, max_length=32, num_beams=1)
sentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
assert sentence == "I am a medical doctor."
```
## 7. Fine-tuning
We also provide a script `train_any_t5_task.py` that might help you fine-tune any Text2Text Task with T5. We added #TODO comments all over to help you use train with ease. For example:
```python
# TODO Set your training epochs
config.TRAIN_EPOCHS = 3
```
If you don't want to read the #TODO comments, just pass in your data like this
```python
# TODO Where is your data ? Enter the path
trainer.start("data/sentence_doctor_dataset_300.csv")
```
and voila!! Please feel free to correct any mistakes in the code and make a pull request.
## 8. Attribution
* [Huggingface](https://huggingface.co/) transformer lib for making this possible
* Abhishek Kumar Mishra's transformer [tutorial](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) on text summarisation. Our training code is just a modified version of their code. So many thanks.
* We finetuned this model from the huggingface hub: WikinewsSum/t5-base-multi-combine-wiki-news. Thanks to the [authors](https://huggingface.co/WikinewsSum)
* We also read a lot of work from [Suraj Patil](https://github.com/patil-suraj)
* No one has been forgotten, hopefully :)
|
zanelim/singbert-lite-sg
|
zanelim
| 2020-12-11T22:05:08Z | 44 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"albert-base-v2",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- albert-base-v2
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "dont play [MASK] leh"
- text: "die [MASK] must try"
---
# Model name
SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the lite-version, which was initialized from [Albert base v2](https://github.com/google-research/albert#albert), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## Intended uses & limitations
#### How to use
```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-lite-sg')
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try[SEP]',
'score': 0.7731555700302124,
'token': 1327,
'token_str': '▁die'},
{'sequence': '[CLS] die also must try[SEP]',
'score': 0.04763784259557724,
'token': 67,
'token_str': '▁also'},
{'sequence': '[CLS] die still must try[SEP]',
'score': 0.01859409362077713,
'token': 174,
'token_str': '▁still'},
{'sequence': '[CLS] die u must try[SEP]',
'score': 0.015824034810066223,
'token': 287,
'token_str': '▁u'},
{'sequence': '[CLS] die is must try[SEP]',
'score': 0.011271446943283081,
'token': 25,
'token_str': '▁is'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh[SEP]',
'score': 0.4365769624710083,
'token': 418,
'token_str': '▁play'},
{'sequence': '[CLS] dont play punk leh[SEP]',
'score': 0.06880936771631241,
'token': 6769,
'token_str': '▁punk'},
{'sequence': '[CLS] dont play game leh[SEP]',
'score': 0.051739856600761414,
'token': 250,
'token_str': '▁game'},
{'sequence': '[CLS] dont play games leh[SEP]',
'score': 0.045703962445259094,
'token': 466,
'token_str': '▁games'},
{'sequence': '[CLS] dont play around leh[SEP]',
'score': 0.013458190485835075,
'token': 140,
'token_str': '▁around'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball[SEP]',
'score': 0.6197211146354675,
'token': 1592,
'token_str': '▁ball'},
{'sequence': '[CLS] catch no balls[SEP]',
'score': 0.08441998809576035,
'token': 7152,
'token_str': '▁balls'},
{'sequence': '[CLS] catch no joke[SEP]',
'score': 0.0676785409450531,
'token': 8186,
'token_str': '▁joke'},
{'sequence': '[CLS] catch no?[SEP]',
'score': 0.040638409554958344,
'token': 60,
'token_str': '?'},
{'sequence': '[CLS] catch no one[SEP]',
'score': 0.03546864539384842,
'token': 53,
'token_str': '▁one'}]
>>> nlp("confirm plus [MASK]")
[{'sequence': '[CLS] confirm plus chop[SEP]',
'score': 0.9608421921730042,
'token': 17144,
'token_str': '▁chop'},
{'sequence': '[CLS] confirm plus guarantee[SEP]',
'score': 0.011784233152866364,
'token': 9120,
'token_str': '▁guarantee'},
{'sequence': '[CLS] confirm plus confirm[SEP]',
'score': 0.010571340098977089,
'token': 10265,
'token_str': '▁confirm'},
{'sequence': '[CLS] confirm plus egg[SEP]',
'score': 0.0033525123726576567,
'token': 6387,
'token_str': '▁egg'},
{'sequence': '[CLS] confirm plus bet[SEP]',
'score': 0.0008760977652855217,
'token': 5676,
'token_str': '▁bet'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('zanelim/singbert-lite-sg')
model = AlbertModel.from_pretrained("zanelim/singbert-lite-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained("zanelim/singbert-lite-sg")
model = TFAlbertModel.from_pretrained("zanelim/singbert-lite-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
## Training procedure
Initialized with [albert base v2](https://github.com/google-research/albert#albert) vocab and checkpoints (pre-trained weights).
Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 4096
* max_seq_length: 128
* num_train_steps: 125000
* num_warmup_steps: 5000
* learning_rate: 0.00176
* hardware: TPU v3-8
|
yuvraj/summarizer-cnndm
|
yuvraj
| 2020-12-11T22:04:58Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- summarization
---
# Summarization
## Model description
BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset
## How to use
PyTorch model available
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm")
AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm")
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)
summarizer("<Text to be summarized>")
## Limitations and bias
Trained on a small dataset
|
twmkn9/albert-base-v2-squad2
|
twmkn9
| 2020-12-11T22:02:54Z | 4,239 | 4 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
This model is [ALBERT base v2](https://huggingface.co/albert-base-v2) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type albert
--model_name_or_path albert-base-v2
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_negative
--save_steps 100000
--train_file $SQUAD_DIR/train-v2.0.json
--predict_file $SQUAD_DIR/dev-v2.0.json
--per_gpu_train_batch_size 8
--num_train_epochs 3
--learning_rate 3e-5
--max_seq_length 384
--doc_stride 128
--output_dir ./tmp/albert_fine/
```
Performance on a dev subset is close to the original paper:
```
Results:
{
'exact': 78.71010200723923,
'f1': 81.89228117126069,
'total': 6078,
'HasAns_exact': 75.39518900343643,
'HasAns_f1': 82.04167868004215,
'HasAns_total': 2910,
'NoAns_exact': 81.7550505050505,
'NoAns_f1': 81.7550505050505,
'NoAns_total': 3168,
'best_exact': 78.72655478775913,
'best_exact_thresh': 0.0,
'best_f1': 81.90873395178066,
'best_f1_thresh': 0.0
}
```
We are hopeful this might save you time, energy, and compute. Cheers!
|
tuner007/t5_abs_qa
|
tuner007
| 2020-12-11T22:02:51Z | 51 | 8 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# T5 for abstractive question-answering
This is T5-base model fine-tuned for abstractive QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 2 epochs
## Model in Action 🚀
```
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("tuner007/t5_abs_qa")
model = AutoModelWithLMHead.from_pretrained("tuner007/t5_abs_qa")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
def get_answer(question, context):
input_text = "context: %s <question for context: %s </s>" % (context,question)
features = tokenizer([input_text], return_tensors='pt')
out = model.generate(input_ids=features['input_ids'].to(device), attention_mask=features['attention_mask'].to(device))
return tokenizer.decode(out[0])
```
#### Example 1: Answer available
```
context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
question = "What is Valhalla?"
get_answer(question, context)
# output: 'It is a hall of worship ruled by Odin.'
```
#### Example 2: Answer not available
```
context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
question = "What is Asgard?"
get_answer(question, context)
# output: 'No answer available in context.'
```
> Created by Arpit Rajauria
[](https://twitter.com/arpit_rajauria)
|
squeezebert/squeezebert-uncased
|
squeezebert
| 2020-12-11T22:02:17Z | 41,687 | 2 |
transformers
|
[
"transformers",
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/).
The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone.
## Pretraining
### Pretraining data
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of thousands of unpublished books
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
### Pretraining procedure
The model is pretrained using the Masked Language Model (MLM) and Sentence Order Prediction (SOP) tasks.
(Author's note: If you decide to pretrain your own model, and you prefer to train with MLM only, that should work too.)
From the SqueezeBERT paper:
> We pretrain SqueezeBERT from scratch (without distillation) using the [LAMB](https://arxiv.org/abs/1904.00962) optimizer, and we employ the hyperparameters recommended by the LAMB authors: a global batch size of 8192, a learning rate of 2.5e-3, and a warmup proportion of 0.28. Following the LAMB paper's recommendations, we pretrain for 56k steps with a maximum sequence length of 128 and then for 6k steps with a maximum sequence length of 512.
## Finetuning
The SqueezeBERT paper results from 2 approaches to finetuning the model:
- "finetuning without bells and whistles" -- after pretraining the SqueezeBERT model, finetune it on each GLUE task
- "finetuning with bells and whistles" -- after pretraining the SqueezeBERT model, finetune it on a MNLI with distillation from a teacher model. Then, use the MNLI-finetuned SqueezeBERT model as a student model to finetune on each of the other GLUE tasks (e.g. RTE, MRPC, …) with distillation from a task-specific teacher model.
A detailed discussion of the hyperparameters used for finetuning is provided in the appendix of the [SqueezeBERT paper](https://arxiv.org/abs/2006.11316).
Note that finetuning SqueezeBERT with distillation is not yet implemented in this repo. If the author (Forrest Iandola - forrest.dnn@gmail.com) gets enough encouragement from the user community, he will add example code to Transformers for finetuning SqueezeBERT with distillation.
This model, `squeezebert/squeezebert-uncased`, has been pretrained but not finetuned. For most text classification tasks, we recommend using squeezebert-mnli-headless as a starting point.
### How to finetune
To try finetuning SqueezeBERT on the [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) text classification task, you can run the following command:
```
./utils/download_glue_data.py
python examples/text-classification/run_glue.py \
--model_name_or_path squeezebert-base-headless \
--task_name mrpc \
--data_dir ./glue_data/MRPC \
--output_dir ./models/squeezebert_mrpc \
--overwrite_output_dir \
--do_train \
--do_eval \
--num_train_epochs 10 \
--learning_rate 3e-05 \
--per_device_train_batch_size 16 \
--save_steps 20000
```
## BibTeX entry and citation info
```
@article{2020_SqueezeBERT,
author = {Forrest N. Iandola and Albert E. Shaw and Ravi Krishna and Kurt W. Keutzer},
title = {{SqueezeBERT}: What can computer vision teach NLP about efficient neural networks?},
journal = {arXiv:2006.11316},
year = {2020}
}
```
|
squeezebert/squeezebert-mnli-headless
|
squeezebert
| 2020-12-11T22:02:10Z | 80 | 0 |
transformers
|
[
"transformers",
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli-headless`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language Inference (MNLI)](https://cims.nyu.edu/~sbowman/multinli/) dataset. This is a "headless" model with the final classification layer removed, and this will allow Transformers to automatically reinitialize the final classification layer before you begin finetuning on your data.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/).
The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone.
## Pretraining
### Pretraining data
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of thousands of unpublished books
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
### Pretraining procedure
The model is pretrained using the Masked Language Model (MLM) and Sentence Order Prediction (SOP) tasks.
(Author's note: If you decide to pretrain your own model, and you prefer to train with MLM only, that should work too.)
From the SqueezeBERT paper:
> We pretrain SqueezeBERT from scratch (without distillation) using the [LAMB](https://arxiv.org/abs/1904.00962) optimizer, and we employ the hyperparameters recommended by the LAMB authors: a global batch size of 8192, a learning rate of 2.5e-3, and a warmup proportion of 0.28. Following the LAMB paper's recommendations, we pretrain for 56k steps with a maximum sequence length of 128 and then for 6k steps with a maximum sequence length of 512.
## Finetuning
The SqueezeBERT paper presents 2 approaches to finetuning the model:
- "finetuning without bells and whistles" -- after pretraining the SqueezeBERT model, finetune it on each GLUE task
- "finetuning with bells and whistles" -- after pretraining the SqueezeBERT model, finetune it on a MNLI with distillation from a teacher model. Then, use the MNLI-finetuned SqueezeBERT model as a student model to finetune on each of the other GLUE tasks (e.g. RTE, MRPC, …) with distillation from a task-specific teacher model.
A detailed discussion of the hyperparameters used for finetuning is provided in the appendix of the [SqueezeBERT paper](https://arxiv.org/abs/2006.11316).
Note that finetuning SqueezeBERT with distillation is not yet implemented in this repo. If the author (Forrest Iandola - forrest.dnn@gmail.com) gets enough encouragement from the user community, he will add example code to Transformers for finetuning SqueezeBERT with distillation.
This model, `squeezebert/squeezebert-mnli-headless`, is the "finetuned with bells and whistles" MNLI-finetuned SqueezeBERT model. In this particular model, we have removed the final classification layer -- in other words, it is "headless." We recommend using this model if you intend to finetune the model on your own data. Using this model means that your final layer will automatically be reinitialized when you start finetuning on your data.
### How to finetune
To try finetuning SqueezeBERT on the [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) text classification task, you can run the following command:
```
./utils/download_glue_data.py
python examples/text-classification/run_glue.py \
--model_name_or_path squeezebert-base-headless \
--task_name mrpc \
--data_dir ./glue_data/MRPC \
--output_dir ./models/squeezebert_mrpc \
--overwrite_output_dir \
--do_train \
--do_eval \
--num_train_epochs 10 \
--learning_rate 3e-05 \
--per_device_train_batch_size 16 \
--save_steps 20000
```
## BibTeX entry and citation info
```
@article{2020_SqueezeBERT,
author = {Forrest N. Iandola and Albert E. Shaw and Ravi Krishna and Kurt W. Keutzer},
title = {{SqueezeBERT}: What can computer vision teach NLP about efficient neural networks?},
journal = {arXiv:2006.11316},
year = {2020}
}
```
|
shoarora/alectra-small-owt
|
shoarora
| 2020-12-11T22:01:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# ALECTRA-small-OWT
This is an extension of
[ELECTRA](https://openreview.net/forum?id=r1xMH1BtvB) small model, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
The training task (discriminative LM / replaced-token-detection) can be generalized to any transformer type. Here, we train an ALBERT model under the same scheme.
## Pretraining task
(figure from [Clark et al. 2020](https://openreview.net/pdf?id=r1xMH1BtvB))
ELECTRA uses discriminative LM / replaced-token-detection for pretraining.
This involves a generator (a Masked LM model) creating examples for a discriminator
to classify as original or replaced for each token.
The generator generalizes to any `*ForMaskedLM` model and the discriminator could be
any `*ForTokenClassification` model. Therefore, we can extend the task to ALBERT models,
not just BERT as in the original paper.
## Usage
```python
from transformers import AlbertForSequenceClassification, BertTokenizer
# Both models use the bert-base-uncased tokenizer and vocab.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
alectra = AlbertForSequenceClassification.from_pretrained('shoarora/alectra-small-owt')
```
NOTE: this ALBERT model uses a BERT WordPiece tokenizer.
## Code
The pytorch module that implements this task is available [here](https://github.com/shoarora/lmtuners/blob/master/lmtuners/lightning_modules/discriminative_lm.py).
Further implementation information [here](https://github.com/shoarora/lmtuners/tree/master/experiments/disc_lm_small),
and [here](https://github.com/shoarora/lmtuners/blob/master/experiments/disc_lm_small/train_alectra_small.py) is the script that created this model.
This specific model was trained with the following params:
- `batch_size: 512`
- `training_steps: 5e5`
- `warmup_steps: 4e4`
- `learning_rate: 2e-3`
## Downstream tasks
#### GLUE Dev results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ELECTRA-Small++ | 14M | 57.0 | 91. | 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT | 14M | 56.8 | 88.3| 87.4 | 86.8 | 88.3 | 78.9 | 87.9 | 68.5|
| ELECTRA-Small-OWT (ours) | 17M | 56.3 | 88.4| 75.0 | 86.1 | 89.1 | 77.9 | 83.0 | 67.1|
| ALECTRA-Small-OWT (ours) | 4M | 50.6 | 89.1| 86.3 | 87.2 | 89.1 | 78.2 | 85.9 | 69.6|
#### GLUE Test results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| BERT-Base | 110M | 52.1 | 93.5| 84.8 | 85.9 | 89.2 | 84.6 | 90.5 | 66.4|
| GPT | 117M | 45.4 | 91.3| 75.7 | 80.0 | 88.5 | 82.1 | 88.1 | 56.0|
| ELECTRA-Small++ | 14M | 57.0 | 91.2| 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT (ours) | 17M | 57.4 | 89.3| 76.2 | 81.9 | 87.5 | 78.1 | 82.4 | 68.1|
| ALECTRA-Small-OWT (ours) | 4M | 43.9 | 87.9| 82.1 | 82.0 | 87.6 | 77.9 | 85.8 | 67.5|
|
patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16
|
patrickvonplaten
| 2020-12-11T21:59:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Shared Roberta2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a shared Roberta2Roberta model, meaning that the encoder and decoder weights are tied, fine-tuned on summarization.
Roberta2Roberta is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `roberta-base`
RoBERTa models. In this setup the encoder and decoder weights are tied. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the
two pretrained models can simply be loaded into the framework via:
```python
roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True)
```
The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal
masking for auto-regressiv generation.
Thus, ``roberta2roberta`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model
`roberta2roberta-share-cnn_dailymail-fp16` is uploaded here.
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable summarization results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 EncoderDecoder Framework.
The model can be used as follows:
```python
from transformers import RobertaTokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16")
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
article = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David B
oren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 185
6, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confede
rate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking fu
ll membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on t
he fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more invol
ved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members al
legedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a frat
ernity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,
' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloy
d's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing in
cidents."""
input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# SAE's national chapter suspended after video shows party-bound fraternity members singing racist chant. University of Oklahoma president says university's affiliation with fraternity is permanently done.
# SAE has had to close 12 chapters since 2010 after members were killed in hazing. The fraternity has had more than 130 chapters in 18 months.
```
## Training script:
**IMPORTANT**: In order for this code to work, make sure you checkout to the branch
[more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts
the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840.
The following code shows the complete training script that was used to fine-tune `roberta2roberta-cnn_dailymail-fp16
` for reproducability. The training last ~9h on a standard GPU.
```python
#!/usr/bin/env python3
import nlp
import logging
from transformers import RobertaTokenizer, EncoderDecoderModel, Trainer, TrainingArguments
logging.basicConfig(level=logging.INFO)
model = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True)
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")
# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=0)
# set decoding params
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
encoder_length = 512
decoder_length = 128
batch_size = 16
# map data correctly
def map_to_encoder_decoder_inputs(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at Longformer at 2048
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
# force summarization <= 256
outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
# mask loss for padding
batch["labels"] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
batch["decoder_attention_mask"] = outputs.attention_mask
assert all([len(x) == encoder_length for x in inputs.input_ids])
assert all([len(x) == decoder_length for x in outputs.input_ids])
return batch
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = tokenizer.eos_token_id
label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
# make train dataset ready
train_dataset = train_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "decoder_attention_mask", "decoder_input_ids", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch", columns=["input_ids", "decoder_attention_mask", "attention_mask", "decoder_input_ids", "labels"],
)
# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
output_dir="./",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_from_generate=True,
evaluate_during_training=True,
do_train=True,
do_eval=True,
logging_steps=1000,
save_steps=1000,
eval_steps=1000,
overwrite_output_dir=True,
warmup_steps=2000,
save_total_limit=3,
fp16=True,
)
# instantiate trainer
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
# start training
trainer.train()
```
## Evaluation
The following script evaluates the model on the test set of
CNN/Daily Mail.
```python
#!/usr/bin/env python3
import nlp
from transformers import RobertaTokenizer, EncoderDecoderModel
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16")
model.to("cuda")
test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 128
# map data correctly
def generate_summary(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at BERT max length 512
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
outputs = model.generate(input_ids, attention_mask=attention_mask)
# all special tokens including will be removed
output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch["pred"] = output_str
return batch
results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
# load rouge for validation
rouge = nlp.load_metric("rouge")
pred_str = results["pred"]
label_str = results["highlights"]
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
print(rouge_output)
```
The obtained results should be:
| - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
|----------|:-------------:|:------:|:------:|
| **CNN/Daily Mail** | 15.6 | 18.79 | **16.59** |
|
patrickvonplaten/longformer2roberta-cnn_dailymail-fp16
|
patrickvonplaten
| 2020-12-11T21:59:19Z | 102 | 6 |
transformers
|
[
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Longformer2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a Longformer2Roberta model fine-tuned on summarization.
Longformer2Roberta is a `EncoderDecoderModel`, meaning that both the encoder is a `allenai/longformer-base-4096` model and the decoder is a `roberta-base` model. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the
two pretrained models can simply be loaded into the framework via:
```python
roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("allenai/longformer-base-4096", "roberta-base")
```
The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal
masking for auto-regressiv generation.
Thus, ``longformer2roberta`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model
`longformer2roberta-cnn_dailymail-fp16` is uploaded here.
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable summarization results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 EncoderDecoder Framework.
The model can be used as follows:
```python
from transformers import LongformerTokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/longformer2roberta-cnn_dailymail-fp16")
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
article = """(CNN)James Holmes made his introduction to the world in a Colorado cinema filled with spectators watching a midnight showing of the new Batman movie, "The Dark Knight Rises," in June 2012. The moment became one of the deadliest shootings in U.S. history. Holmes is accused of opening fire on the crowd, killing 12 people and injuring or maiming 70 others in Aurora, a suburb of Denver. Holmes appeared like a comic book character: He resembled the Joker, with red-orange hair, similar to the late actor Heath Ledger\'s portrayal of the villain in an earlier Batman movie, authorities said. But Holmes was hardly a cartoon. Authorities said he wore body armor and carried several guns, including an AR-15 rifle, with lots of ammo. He also wore a gas mask. Holmes says he was insane at the time of the shootings, and that is his legal defense and court plea: not guilty by reason of insanity. Prosecutors aren\'t swayed and will seek the death penalty. Opening statements in his trial are scheduled to begin Monday. Holmes admits to the shootings but says he was suffering "a psychotic episode" at the time, according to court papers filed in July 2013 by the state public defenders, Daniel King and Tamara A. Brady. Evidence "revealed thus far in the case supports the defense\'s position that Mr. Holmes suffers from a severe mental illness and was in the throes of a psychotic episode when he committed the acts that resulted in the tragic loss of life and injuries sustained by moviegoers on July 20, 2012," the public defenders wrote. Holmes no longer looks like a dazed Joker, as he did in his first appearance before a judge in 2012. He appeared dramatically different in January when jury selection began for his trial: 9,000 potential jurors were summoned for duty, described as one of the nation\'s largest jury calls. Holmes now has a cleaner look, with a mustache, button-down shirt and khaki pants. In January, he had a beard and eyeglasses. If this new image sounds like one of an academician, it may be because Holmes, now 27, once was one. Just before the shooting, Holmes was a doctoral student in neuroscience, and he was studying how the brain works, with his schooling funded by a U.S. government grant. Yet for all his learning, Holmes apparently lacked the capacity to command his own mind, according to the case against him. A jury will ultimately decide Holmes\' fate. That panel is made up of 12 jurors and 12 alternates. They are 19 women and five men, and almost all are white and middle-aged. The trial could last until autumn. When jury summonses were issued in January, each potential juror stood a 0.2% chance of being selected, District Attorney George Brauchler told the final jury this month. He described the approaching trial as "four to five months of a horrible roller coaster through the worst haunted house you can imagine." The jury will have to render verdicts on each of the 165 counts against Holmes, including murder and attempted murder charges. Meanwhile, victims and their relatives are challenging all media outlets "to stop the gratuitous use of the name and likeness of mass killers, thereby depriving violent individuals the media celebrity and media spotlight they so crave," the No Notoriety group says. They are joined by victims from eight other mass shootings in recent U.S. history. Raised in central coastal California and in San Diego, James Eagan Holmes is the son of a mathematician father noted for his work at the FICO firm that provides credit scores and a registered nurse mother, according to the U-T San Diego newspaper. Holmes also has a sister, Chris, a musician, who\'s five years younger, the newspaper said. His childhood classmates remember him as a clean-cut, bespectacled boy with an "exemplary" character who "never gave any trouble, and never got in trouble himself," The Salinas Californian reported. His family then moved down the California coast, where Holmes grew up in the San Diego-area neighborhood of Rancho Peñasquitos, which a neighbor described as "kind of like Mayberry," the San Diego newspaper said. Holmes attended Westview High School, which says its school district sits in "a primarily middle- to upper-middle-income residential community." There, Holmes ran cross-country, played soccer and later worked at a biotechnology internship at the Salk Institute and Miramar College, which attracts academically talented students. By then, his peers described him as standoffish and a bit of a wiseacre, the San Diego newspaper said. Holmes attended college fairly close to home, in a neighboring area known as Southern California\'s "inland empire" because it\'s more than an hour\'s drive from the coast, in a warm, low-desert climate. He entered the University of California, Riverside, in 2006 as a scholarship student. In 2008 he was a summer camp counselor for disadvantaged children, age 7 to 14, at Camp Max Straus, run by Jewish Big Brothers Big Sisters of Los Angeles. He graduated from UC Riverside in 2010 with the highest honors and a bachelor\'s degree in neuroscience. "Academically, he was at the top of the top," Chancellor Timothy P. White said. He seemed destined for even higher achievement. By 2011, he had enrolled as a doctoral student in the neuroscience program at the University of Colorado Anschutz Medical Campus in Aurora, the largest academic health center in the Rocky Mountain region. The doctoral in neuroscience program attended by Holmes focuses on how the brain works, with an emphasis on processing of information, behavior, learning and memory. Holmes was one of six pre-thesis Ph.D. students in the program who were awarded a neuroscience training grant from the National Institutes of Health. The grant rewards outstanding neuroscientists who will make major contributions to neurobiology. A syllabus that listed Holmes as a student at the medical school shows he was to have delivered a presentation about microRNA biomarkers. But Holmes struggled, and his own mental health took an ominous turn. In March 2012, he told a classmate he wanted to kill people, and that he would do so "when his life was over," court documents said. Holmes was "denied access to the school after June 12, 2012, after he made threats to a professor," according to court documents. About that time, Holmes was a patient of University of Colorado psychiatrist Lynne Fenton. Fenton was so concerned about Holmes\' behavior that she mentioned it to her colleagues, saying he could be a danger to others, CNN affiliate KMGH-TV reported, citing sources with knowledge of the investigation. Fenton\'s concerns surfaced in early June, sources told the Denver station. Holmes began to fantasize about killing "a lot of people" in early June, nearly six weeks before the shootings, the station reported, citing unidentified sources familiar with the investigation. Holmes\' psychiatrist contacted several members of a "behavioral evaluation and threat assessment" team to say Holmes could be a danger to others, the station reported. At issue was whether to order Holmes held for 72 hours to be evaluated by mental health professionals, the station reported. "Fenton made initial phone calls about engaging the BETA team" in "the first 10 days" of June, but it "never came together" because in the period Fenton was having conversations with team members, Holmes began the process of dropping out of school, a source told KMGH. Defense attorneys have rejected the prosecution\'s assertions that Holmes was barred from campus. Citing statements from the university, Holmes\' attorneys have argued that his access was revoked because that\'s normal procedure when a student drops enrollment. What caused this turn for the worse for Holmes has yet to be clearly detailed. In the months before the shooting, he bought four weapons and more than 6,000 rounds of ammunition, authorities said. Police said he also booby-trapped his third-floor apartment with explosives, but police weren\'t fooled. After Holmes was caught in the cinema parking lot immediately after the shooting, bomb technicians went to the apartment and neutralized the explosives. No one was injured at the apartment building. Nine minutes before Holmes went into the movie theater, he called a University of Colorado switchboard, public defender Brady has said in court. The number he called can be used to get in contact with faculty members during off hours, Brady said. Court documents have also revealed that investigators have obtained text messages that Holmes exchanged with someone before the shooting. That person was not named, and the content of the texts has not been made public. According to The New York Times, Holmes sent a text message to a fellow graduate student, a woman, about two weeks before the shooting. She asked if he had left Aurora yet, reported the newspaper, which didn\'t identify her. No, he had two months left on his lease, Holmes wrote back, according to the Times. He asked if she had heard of "dysphoric mania," a form of bipolar disorder marked by the highs of mania and the dark and sometimes paranoid delusions of major depression. The woman asked if the disorder could be managed with treatment. "It was," Holmes wrote her, according to the Times. But he warned she should stay away from him "because I am bad news," the newspaper reported. It was her last contact with Holmes. After the shooting, Holmes\' family issued a brief statement: "Our hearts go out to those who were involved in this tragedy and to the families and friends of those involved," they said, without giving any information about their son. Since then, prosecutors have refused to offer a plea deal to Holmes. For Holmes, "justice is death," said Brauchler, the district attorney. In December, Holmes\' parents, who will be attending the trial, issued another statement: They asked that their son\'s life be spared and that he be sent to an institution for mentally ill people for the rest of his life, if he\'s found not guilty by reason of insanity. "He is not a monster," Robert and Arlene Holmes wrote, saying the death penalty is "morally wrong, especially when the condemned is mentally ill." "He is a human being gripped by a severe mental illness," the parents said. The matter will be settled by the jury. CNN\'s Ana Cabrera and Sara Weisfeldt contributed to this report from Denver."""
input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# James Holmes, 27, is accused of opening fire on a Colorado theater.
# He was a doctoral student at University of Colorado.
# Holmes says he was suffering "a psychotic episode" at the time of the shooting.
# Prosecutors won't say whether Holmes was barred from campus.
```
Such an article has a length of > 2000 tokens, which means that it cannot be handled correctly by Bert or Roberta encoders.
## Training script:
**IMPORTANT**: In order for this code to work, make sure you checkout to the branch
[more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts
the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840.
The following code shows the complete training script that was used to fine-tune `longformer2roberta-cnn_dailymail-fp16
` for reproducability. The training last ~90h on a standard GPU.
```python
#!/usr/bin/env python3
import nlp
import logging
from transformers import LongformerTokenizer, EncoderDecoderModel, Trainer, TrainingArguments
logging.basicConfig(level=logging.INFO)
model = EncoderDecoderModel.from_encoder_decoder_pretrained("allenai/longformer-base-4096", "roberta-base")
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")
# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=0)
# enable gradient checkpointing for longformer encoder
model.encoder.config.gradient_checkpointing = True
# set decoding params
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
encoder_length = 2048
decoder_length = 128
batch_size = 16
# map data correctly
def map_to_encoder_decoder_inputs(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at Longformer at 2048
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
# force summarization <= 128
outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
# set 128 tokens to global attention
batch["global_attention_mask"] = [[1 if i < 128 else 0 for i in range(sequence_length)] for sequence_length in len(inputs.input_ids) * [encoder_length]]
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
# mask loss for padding
batch["labels"] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
batch["decoder_attention_mask"] = outputs.attention_mask
assert all([len(x) == encoder_length for x in inputs.input_ids])
assert all([len(x) == decoder_length for x in outputs.input_ids])
return batch
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = tokenizer.eos_token_id
label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
# make train dataset ready
train_dataset = train_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch", columns=["input_ids", "global_attention_mask", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
output_dir="./",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_from_generate=True,
evaluate_during_training=True,
do_train=True,
do_eval=True,
logging_steps=1000,
save_steps=1000,
eval_steps=1000,
overwrite_output_dir=True,
warmup_steps=2000,
save_total_limit=3,
fp16=True,
)
# instantiate trainer
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
# start training
trainer.train()
```
## Evaluation
The following script evaluates the model on the test set of
CNN/Daily Mail.
```python
#!/usr/bin/env python3
import nlp
import torch
from transformers import LongformerTokenizer, EncoderDecoderModel
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/longformer2roberta-cnn_dailymail-fp16")
model.to("cuda")
test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 32
encoder_length = 2048
decoder_length = 128
# map data correctly
def generate_summary(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at BERT max length 512
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
global_attention_mask = torch.zeros_like(attention_mask)
global_attention_mask[:, :decoder_length] = 1
outputs = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
# all special tokens including will be removed
output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch["pred"] = output_str
return batch
results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
# load rouge for validation
rouge = nlp.load_metric("rouge")
pred_str = results["pred"]
label_str = results["highlights"]
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
print(rouge_output)
```
The obtained results should be:
| - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
|----------|:-------------:|:------:|:------:|
| **CNN/Daily Mail** | 12.39 | 15.05 | **13.21** |
**Note** This model was trained to show how Longformer can be used as an Encoder model in a EncoderDecoder setup.
Better results are obtained for datasets of much longer inputs.
|
mrm8488/xlm-multi-finetuned-xquadv1
|
mrm8488
| 2020-12-11T21:56:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm",
"question-answering",
"multilingual",
"arxiv:1901.07291",
"arxiv:1910.11856",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: multilingual
thumbnail:
---
# [XLM](https://github.com/facebookresearch/XLM/) (multilingual version) fine-tuned for multilingual Q&A
Released from `Facebook` together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.
## Details of the language model('xlm-mlm-100-1280')
[Language model](https://github.com/facebookresearch/XLM/#ii-cross-lingual-language-model-pretraining-xlm)
| Languages
| --------- |
| 100 |
It includes the following languages:
<details>
en-es-fr-de-zh-ru-pt-it-ar-ja-id-tr-nl-pl-simple-fa-vi-sv-ko-he-ro-no-hi-uk-cs-fi-hu-th-da-ca-el-bg-sr-ms-bn-hr-sl-zh_yue-az-sk-eo-ta-sh-lt-et-ml-la-bs-sq-arz-af-ka-mr-eu-tl-ang-gl-nn-ur-kk-be-hy-te-lv-mk-zh_classical-als-is-wuu-my-sco-mn-ceb-ast-cy-kn-br-an-gu-bar-uz-lb-ne-si-war-jv-ga-zh_min_nan-oc-ku-sw-nds-ckb-ia-yi-fy-scn-gan-tt-am
</details>
## Details of the downstream task (multilingual Q&A) - Dataset
Deepmind [XQuAD](https://github.com/deepmind/xquad)
Languages covered:
- Arabic: `ar`
- German: `de`
- Greek: `el`
- English: `en`
- Spanish: `es`
- Hindi: `hi`
- Russian: `ru`
- Thai: `th`
- Turkish: `tr`
- Vietnamese: `vi`
- Chinese: `zh`
As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this
setting so that models can focus on cross-lingual transfer.
We show the average number of tokens per paragraph, question, and answer for each language in the
table below. The statistics were obtained using [Jieba](https://github.com/fxsjy/jieba) for Chinese
and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
for the other languages.
| | en | es | de | el | ru | tr | ar | vi | th | zh | hi |
| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
| Question | 11.5 | 13.4 | 11.0 | 11.7 | 10.0 | 9.8 | 10.7 | 14.8 | 11.5 | 10.5 | 18.7 |
| Answer | 3.1 | 3.6 | 3.0 | 3.3 | 3.1 | 3.1 | 3.1 | 4.5 | 4.1 | 3.5 | 5.6 |
Citation:
<details>
```bibtex
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
```
</details>
As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and split the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
| Dataset | # samples |
| ----------- | --------- |
| XQUAD train | 50 K |
| XQUAD test | 8 K |
## Model training
The model was trained on a Tesla P100 GPU and 25GB of RAM.
The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="mrm8488/xlm-multi-finetuned-xquadv1",
tokenizer="mrm8488/xlm-multi-finetuned-xquadv1"
)
# English
qa_pipeline({
'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
'question': "Who has been working hard for hugginface/transformers lately?"
})
#Output: {'answer': 'Manuel', 'end': 6, 'score': 8.531880747878265e-05, 'start': 0}
# Russian
qa_pipeline({
'context': "Мануэль Ромеро в последнее время почти не работал в репозитории hugginface / transformers",
'question': "Кто в последнее время усердно работал над обнимашками / трансформерами?"
})
#Output: {'answer': 'работал в репозитории hugginface /','end': 76, 'score': 0.00012340750456964894, 'start': 42}
```
Try it on a Colab (*Do not forget to change the model and tokenizer path in the Colab if necessary*):
<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Try_mrm8488_xquad_finetuned_uncased_model.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.