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|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test31 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test32 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test33 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test34 | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test35 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test36 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test4 | null | [
"transformers",
"pytorch",
"bart",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test5 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test6 | null | [
"transformers",
"pytorch",
"bart",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test7 | null | [
"transformers",
"pytorch",
"bart",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test8 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-on-patent-Deepspeed-Test9 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | {} | Pyke/bart-finetuned-with-patent-test | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | This model is finetuned by Qichang Zheng(Pyke) based on bart with patent abstract dataset(7 million records), with 'facebook/bart-base' being the tokenizer and original model. The input is the same as the output, which is the patent abstract.
This model is finetuned to serve as a reference to the research that Qichang is in. | {} | Pyke/bart-finetuned-with-patent | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Pyroghy/DialoGPT-Rin-Tohsaka | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Pyroghy/DialoGPT-small-rin_tohsaka | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/Ab | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/AbkPre | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/Abkh | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/Abkha | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/AbkhazPredict | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/AbkhazPrediction | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/Abkhi | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/abk-eng | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/opus-mt-ab-en | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QA/your-model-name | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers |
Propaganda Techniques Analysis BERT
----
This model is a BERT based model to make predictions of propaganda techniques in
news articles in English. The model is described in
[this paper](https://propaganda.qcri.org/papers/EMNLP_2019__Fine_Grained_Propaganda_Detection.pdf).
## Model description
Please find propaganda definition here:
https://propaganda.qcri.org/annotations/definitions.html
You can also try the model in action here: https://www.tanbih.org/prta
### How to use
```python
>>> from transformers import BertTokenizerFast
>>> from .model import BertForTokenAndSequenceJointClassification
>>>
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')
>>> model = BertForTokenAndSequenceJointClassification.from_pretrained(
>>> "QCRI/PropagandaTechniquesAnalysis-en-BERT",
>>> revision="v0.1.0",
>>> )
>>>
>>> inputs = tokenizer.encode_plus("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> sequence_class_index = torch.argmax(outputs.sequence_logits, dim=-1)
>>> sequence_class = model.sequence_tags[sequence_class_index[0]]
>>> token_class_index = torch.argmax(outputs.token_logits, dim=-1)
>>> tokens = tokenizer.convert_ids_to_tokens(inputs.input_ids[0][1:-1])
>>> tags = [model.token_tags[i] for i in token_class_index[0].tolist()[1:-1]]
```
### BibTeX entry and citation info
```bibtex
@inproceedings{da-san-martino-etal-2019-fine,
title = "Fine-Grained Analysis of Propaganda in News Article",
author = "Da San Martino, Giovanni and
Yu, Seunghak and
Barr{\'o}n-Cede{\~n}o, Alberto and
Petrov, Rostislav and
Nakov, Preslav",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1565",
doi = "10.18653/v1/D19-1565",
pages = "5636--5646",
abstract = "Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.",
}
```
| {"language": "en", "license": "MIT", "tags": ["propaganda", "bert"], "datasets": [], "metrics": [], "thumbnail": "https://pbs.twimg.com/profile_images/1092721745994440704/d6R-AHzj_400x400.jpg"} | QCRI/PropagandaTechniquesAnalysis-en-BERT | null | [
"transformers",
"pytorch",
"bert",
"propaganda",
"en",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | QE/numerai_statistics | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QQ/scarlett | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Qasim/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | QianWeiTech/GPT2-News | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | QianWeiTech/GPT2-Titles | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | Qiaozhen/fake-news-detector | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36769078
- CO2 Emissions (in grams): 23.42719853096565
## Validation Metrics
- Loss: 0.15959647297859192
- Accuracy: 0.9817757009345794
- Precision: 0.980411361410382
- Recall: 0.9813725490196078
- AUC: 0.9982379201680672
- F1: 0.9808917197452229
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Qinghui/autonlp-fake-covid-news-36769078
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Qinghui/autonlp-fake-covid-news-36769078", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Qinghui/autonlp-fake-covid-news-36769078", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Qinghui/autonlp-data-fake-covid-news"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 23.42719853096565} | Qinghui/autonlp-fake-covid-news-36769078 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:Qinghui/autonlp-data-fake-covid-news",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | # Punctuator for Uncased English
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (uncased English)
## Usage
```python
from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
model = DistilBertForTokenClassification.from_pretrained("Qishuai/distilbert_punctuator_en")
tokenizer = DistilBertTokenizerFast.from_pretrained("Qishuai/distilbert_punctuator_en")
```
## Model Overview
### Training data
Combination of following three dataset:
- BBC news: From BBC news website corresponding to stories in five topical areas from 2004-2005. [Reference](https://www.kaggle.com/hgultekin/bbcnewsarchive)
- News articles: 20000 samples of short news articles scraped from Hindu, Indian times and Guardian between Feb 2017 and Aug 2017 [Reference](https://www.kaggle.com/sunnysai12345/news-summary?select=news_summary_more.csv)
- Ted talks: transcripts of over 4,000 TED talks between 2004 and 2019 [Reference](https://www.kaggle.com/miguelcorraljr/ted-ultimate-dataset)
### Model Performance
- Validation with 500 samples of dataset scraped from https://www.thenews.com.pk website. [Reference](https://www.kaggle.com/asad1m9a9h6mood/news-articles)
- Metrics Report:
| | precision | recall | f1-score | support |
|:--------------:|:---------:|:------:|:--------:|:-------:|
| COMMA | 0.66 | 0.55 | 0.60 | 7064 |
| EXLAMATIONMARK | 1.00 | 0.00 | 0.00 | 5 |
| PERIOD | 0.73 | 0.63 | 0.68 | 6573 |
| QUESTIONMARK | 0.54 | 0.41 | 0.47 | 17 |
| micro avg | 0.69 | 0.59 | 0.64 | 13659 |
| macro avg | 0.73 | 0.40 | 0.44 | 13659 |
| weighted avg | 0.69 | 0.59 | 0.64 | 13659 |
- Validation with 86 news ted talks of 2020 which are not included in training dataset [Reference](https://www.kaggle.com/thegupta/ted-talk)
- Metrics Report:
| | precision | recall | f1-score | support |
|:--------------:|:---------:|:------:|:--------:|:-------:|
| COMMA | 0.71 | 0.56 | 0.63 | 10712 |
| EXLAMATIONMARK | 0.45 | 0.07 | 0.12 | 75 |
| PERIOD | 0.75 | 0.65 | 0.70 | 7921 |
| QUESTIONMARK | 0.73 | 0.67 | 0.70 | 827 |
| micro avg | 0.73 | 0.60 | 0.66 | 19535 |
| macro avg | 0.66 | 0.49 | 0.53 | 19535 |
| weighted avg | 0.73 | 0.60 | 0.66 | 19535 |
| {} | Qishuai/distilbert_punctuator_en | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | # Punctuator for Simplified Chinese
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (simplified Chinese). The model is fine-tuned based on distilled model `bert-base-chinese`.
## Usage
```python
from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
model = DistilBertForTokenClassification.from_pretrained("Qishuai/distilbert_punctuator_zh")
tokenizer = DistilBertTokenizerFast.from_pretrained("Qishuai/distilbert_punctuator_zh")
```
## Model Overview
### Training data
Combination of following three dataset:
- News articles of People's Daily 2014. [Reference](https://github.com/InsaneLife/ChineseNLPCorpus)
### Model Performance
- Validation with MSRA training dataset. [Reference](https://github.com/InsaneLife/ChineseNLPCorpus/tree/master/NER/MSRA)
- Metrics Report:
| | precision | recall | f1-score | support |
|:----------------:|:---------:|:------:|:--------:|:-------:|
| C_COMMA | 0.67 | 0.59 | 0.63 | 91566 |
| C_DUNHAO | 0.50 | 0.37 | 0.42 | 21013 |
| C_EXLAMATIONMARK | 0.23 | 0.06 | 0.09 | 399 |
| C_PERIOD | 0.84 | 0.99 | 0.91 | 44258 |
| C_QUESTIONMARK | 0.00 | 1.00 | 0.00 | 0 |
| micro avg | 0.71 | 0.67 | 0.69 | 157236 |
| macro avg | 0.45 | 0.60 | 0.41 | 157236 |
| weighted avg | 0.69 | 0.67 | 0.68 | 157236 |
| {} | Qishuai/distilbert_punctuator_zh | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | QiunanLiu/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QueenIonna/Taeyong | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QuentinColdwater/DialoGPT-small-coldwater | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | QuentinColdwater/DialoGPT-small-quentincoldwater | null | [
"transformers",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QuentinColdwater/q_coldwater_chatbot | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | QuentinColdwater/quentin_chatbot | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Quick/mindall-e | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers | Testing PPO-trainer
| {} | QuickRead/PPO_training | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
feature-extraction | transformers | {} | QuickRead/Reward_training_Pegasus_xsum | null | [
"transformers",
"pytorch",
"pegasus",
"feature-extraction",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tune-Pegasus
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3242
- Rouge1: 17.993
- Rouge2: 2.9392
- Rougel: 12.313
- Rougelsum: 13.3091
- Gen Len: 67.0552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.35e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "fine-tune-Pegasus", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"}, "metrics": [{"type": "rouge", "value": 17.993, "name": "Rouge1"}]}]}]} | QuickRead/fine-tune-Pegasus | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | {} | QuickRead/pegasus-reddit-full | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-reddit
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the reddit dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3329
- Rouge1: 23.967
- Rouge2: 5.0032
- Rougel: 15.3267
- Rougelsum: 18.5905
- Gen Len: 69.2193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.35e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "datasets": ["reddit"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-reddit", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "reddit", "type": "reddit", "args": "default"}, "metrics": [{"type": "rouge", "value": 23.967, "name": "Rouge1"}]}]}]} | QuickRead/pegasus-reddit | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:reddit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Quin/Kenneth | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Quindence/DialoGPT-small-LaytonBot | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Qwq/Qq | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | RAPIDS/distilbert-cyberlogs | null | [
"transformers",
"pytorch",
"distilbert",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | RAPIDS/electra-cyberlogs | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RAQA/hshhdhdddd | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RASMUS/norwegian-roberta-base | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-1b-et-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-et-lm-1B
This model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.
It achieves the following results on the test set:
(Loss reported with last eval step at step 2000/2040 during training)
- Loss: 0.2150
- Wer: 0.2012
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"language": "et", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_8_0", "audio", "automatic-speech-recognition", "speech", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Estonian by Rasmus Toivanen", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "et"}, "metrics": [{"type": "wer", "value": 20.12, "name": "Test WER"}, {"type": "cer", "value": 3.82, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "et"}, "metrics": [{"type": "wer", "value": 40.77, "name": "Test WER"}, {"type": "cer", "value": 12.32, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "et"}, "metrics": [{"type": "wer", "value": 41.97, "name": "Test WER"}]}]}]} | RASMUS/wav2vec2-xlsr-1b-et | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_8_0",
"audio",
"speech",
"robust-speech-event",
"hf-asr-leaderboard",
"et",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-1b-ru
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- Wer: 0.0971
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.5462 | 0.35 | 500 | 0.4027 | 0.3575 |
| 0.498 | 0.69 | 1000 | 0.2588 | 0.2513 |
| 0.4279 | 1.04 | 1500 | 0.2265 | 0.2204 |
| 0.4099 | 1.38 | 2000 | 0.2189 | 0.1979 |
| 0.4688 | 1.73 | 2500 | 0.2100 | 0.1920 |
| 0.2241 | 2.07 | 3000 | 0.1980 | 0.1767 |
| 0.2056 | 2.42 | 3500 | 0.2020 | 0.1683 |
| 0.3423 | 2.76 | 4000 | 0.1862 | 0.1606 |
| 0.2478 | 3.11 | 4500 | 0.1787 | 0.1563 |
| 0.3079 | 3.45 | 5000 | 0.1759 | 0.1555 |
| 0.2477 | 3.8 | 5500 | 0.1713 | 0.1423 |
| 0.1718 | 4.14 | 6000 | 0.1695 | 0.1391 |
| 0.1675 | 4.49 | 6500 | 0.1677 | 0.1372 |
| 0.1631 | 4.83 | 7000 | 0.1652 | 0.1333 |
| 0.1429 | 5.18 | 7500 | 0.1605 | 0.1308 |
| 0.1505 | 5.52 | 8000 | 0.1612 | 0.1245 |
| 0.1385 | 5.87 | 8500 | 0.1487 | 0.1225 |
| 0.1285 | 6.22 | 9000 | 0.1526 | 0.1201 |
| 0.1153 | 6.56 | 9500 | 0.1464 | 0.1172 |
| 0.1159 | 6.91 | 10000 | 0.1505 | 0.1143 |
| 0.1061 | 7.25 | 10500 | 0.1444 | 0.1106 |
| 0.1016 | 7.6 | 11000 | 0.1427 | 0.1075 |
| 0.1125 | 7.94 | 11500 | 0.1386 | 0.1045 |
| 0.0937 | 8.29 | 12000 | 0.1403 | 0.1022 |
| 0.1059 | 8.63 | 12500 | 0.1406 | 0.1022 |
| 0.0857 | 8.98 | 13000 | 0.1372 | 0.0992 |
| 0.0901 | 9.32 | 13500 | 0.1380 | 0.0977 |
| 0.0913 | 9.67 | 14000 | 0.1352 | 0.0971 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"language": "ru", "tags": ["audio", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "speech"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Russian by Rasmus Toivanen", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ru"}, "metrics": [{"type": "wer", "value": 10.83, "name": "Test WER"}, {"type": "cer", "value": 2.41, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 37.71, "name": "Test WER"}, {"type": "cer", "value": 12.98, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 31.89, "name": "Test WER"}]}]}]} | RASMUS/wav2vec2-xlsr-1b-ru | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"speech",
"ru",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-300-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-300-versatile-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-300 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-300m-et | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-fi-lm-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common voice train/dev/other datasets.
It achieves the following results on the evaluation set without language model:
- Loss: 0.1853
- Wer: 0.2205
With language model:
- Wer: 0.1026
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8158 | 0.67 | 400 | 0.4835 | 0.6310 |
| 0.5679 | 1.33 | 800 | 0.4806 | 0.5538 |
| 0.6055 | 2.0 | 1200 | 0.3888 | 0.5083 |
| 0.5353 | 2.67 | 1600 | 0.3258 | 0.4365 |
| 0.4883 | 3.33 | 2000 | 0.3313 | 0.4204 |
| 0.4513 | 4.0 | 2400 | 0.2924 | 0.3904 |
| 0.3753 | 4.67 | 2800 | 0.2593 | 0.3608 |
| 0.3478 | 5.33 | 3200 | 0.2832 | 0.3551 |
| 0.3796 | 6.0 | 3600 | 0.2495 | 0.3402 |
| 0.2556 | 6.67 | 4000 | 0.2342 | 0.3106 |
| 0.229 | 7.33 | 4400 | 0.2181 | 0.2812 |
| 0.205 | 8.0 | 4800 | 0.2041 | 0.2523 |
| 0.1654 | 8.67 | 5200 | 0.2015 | 0.2416 |
| 0.152 | 9.33 | 5600 | 0.1942 | 0.2294 |
| 0.1569 | 10.0 | 6000 | 0.1853 | 0.2205 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"language": ["fi"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "model-index": [{"name": "wav2vec2-xlsr-fi-lm-1B", "results": []}]} | RASMUS/wav2vec2-xlsr-fi-lm-1B | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"fi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-fi-train-aug-lm-1B
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Wer: 0.1955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6473 | 0.29 | 400 | 0.2857 | 0.3825 |
| 0.6039 | 0.58 | 800 | 0.2459 | 0.3476 |
| 0.4757 | 0.87 | 1200 | 0.2338 | 0.3274 |
| 0.4473 | 1.15 | 1600 | 0.2246 | 0.3128 |
| 0.4322 | 1.44 | 2000 | 0.1962 | 0.2805 |
| 0.3961 | 1.73 | 2400 | 0.2070 | 0.2797 |
| 0.3642 | 2.02 | 2800 | 0.1790 | 0.2473 |
| 0.3561 | 2.31 | 3200 | 0.1769 | 0.2375 |
| 0.282 | 2.6 | 3600 | 0.1672 | 0.2263 |
| 0.2978 | 2.89 | 4000 | 0.1636 | 0.2192 |
| 0.2722 | 3.17 | 4400 | 0.1637 | 0.2102 |
| 0.2924 | 3.46 | 4800 | 0.1506 | 0.2021 |
| 0.2631 | 3.75 | 5200 | 0.1499 | 0.1955 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"language": "fi", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_7_0", "audio", "automatic-speech-recognition", "speech"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"]} | RASMUS/wav2vec2-xlsr-fi-train-aug-bigLM-1B | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_7_0",
"audio",
"speech",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers | {} | RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B-lower-lr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-fi-train-aug-lm-1B
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Wer: 0.1955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6473 | 0.29 | 400 | 0.2857 | 0.3825 |
| 0.6039 | 0.58 | 800 | 0.2459 | 0.3476 |
| 0.4757 | 0.87 | 1200 | 0.2338 | 0.3274 |
| 0.4473 | 1.15 | 1600 | 0.2246 | 0.3128 |
| 0.4322 | 1.44 | 2000 | 0.1962 | 0.2805 |
| 0.3961 | 1.73 | 2400 | 0.2070 | 0.2797 |
| 0.3642 | 2.02 | 2800 | 0.1790 | 0.2473 |
| 0.3561 | 2.31 | 3200 | 0.1769 | 0.2375 |
| 0.282 | 2.6 | 3600 | 0.1672 | 0.2263 |
| 0.2978 | 2.89 | 4000 | 0.1636 | 0.2192 |
| 0.2722 | 3.17 | 4400 | 0.1637 | 0.2102 |
| 0.2924 | 3.46 | 4800 | 0.1506 | 0.2021 |
| 0.2631 | 3.75 | 5200 | 0.1499 | 0.1955 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"language": "fi", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_7_0", "audio", "automatic-speech-recognition", "speech", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Finnish by Rasmus Toivanen", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 10.96, "name": "Test WER"}, {"type": "cer", "value": 2.81, "name": "Test CER"}]}]}]} | RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_7_0",
"audio",
"speech",
"robust-speech-event",
"hf-asr-leaderboard",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | RASMUS/wav2vec2-xlsr-fi-train-aug-lm-aalto-10k-1B | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RASMUS/wav2vec2-xlsr-fi-train-aug-lm-aalto-full-1B | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | RAhul03/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# chatbot | {"tags": ["conversational"]} | REAP3R/Chat-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Saitama DialoGPT Model | {"tags": ["conversational"]} | REZERO/DialoGPT-medium-saitama | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | RICH双子 | {} | RICH/rui-test | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | this is a test by rui | {} | RICH/test | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | Try the test sentence:
<i>The woman said "my name is Sarah [and] I live in London."</i>
The model should tag the tokens in the sentence with information about whether or not they are contained within a compound clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (http://rgcl.wlv.ac.uk/~richard/Evans2020_SentenceSimplificationForTextProcessing.pdf)
There you will find more information about the tagging scheme.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/CCVspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | This model identifies compound nouns in input sentences.
Try the test sentence:
I love apples [and] potatoes.
Accuracy is best when you place square brackets around the coordinating conjunction.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/CLNspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | This model identifies compound noun phrases in an input sentence.
Try the test sentence:
The inquiry, which continues, will recall John Smith [and] Peter Montgomery next month for further questioning.
Note that you need square brackets around the conjunction coordinating the NPs.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/CMN1spanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence.
Try the test sentence:
John kicked the ball [and] chased after it.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/CMV1spanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | Try the test sentences:
<i>My name is Sarah and I live in London[, which] is the largest city in the UK.</i>
<i>John thought that that was a strange idea.</i>
<i>It was on Tuesdays when Peter took Tess for a walk.</i>
<i>John was so large that he had to crouch to fit through the front door.</i>
The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents.
If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (http://rgcl.wlv.ac.uk/~richard/Evans2020_SentenceSimplificationForTextProcessing.pdf)
There you will find more information about the tagging scheme. | {} | RJ3vans/13.05.2022.SSCCVspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
Try the test sentence:
My name is Sarah and I live in London[,] the capital of England.
Note that accuracy is greatly improved if you place square brackets around the left boundary of the non-finite nominal clause.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/SSMNspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | This model is used to tag the tokens in an input sequence with information about the different signs of syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis (http://rgcl.wlv.ac.uk/~richard/Evans2020_SentenceSimplificationForTextProcessing.pdf).
It was derived using code written by Dr. Le An Ha at the University of Wolverhampton.
To use this model, the following code snippet may help:
======================================================================
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
SignTaggingModel = AutoModelForTokenClassification.from_pretrained('RJ3vans/SignTagger')
SignTaggingTokenizer = AutoTokenizer.from_pretrained('RJ3vans/SignTagger')
label_list = ["M:N_CCV", "M:N_CIN", "M:N_CLA", "M:N_CLAdv", "M:N_CLN", "M:N_CLP", # This could be obtained from the config file
"M:N_CLQ", "M:N_CLV", "M:N_CMA1", "M:N_CMAdv", "M:N_CMN1",
"M:N_CMN2", "M:N_CMN3", "M:N_CMN4", "M:N_CMP", "M:N_CMP2",
"M:N_CMV1", "M:N_CMV2", "M:N_CMV3", "M:N_COMBINATORY", "M:N_CPA",
"M:N_ESAdvP", "M:N_ESCCV", "M:N_ESCM", "M:N_ESMA", "M:N_ESMAdvP",
"M:N_ESMI", "M:N_ESMN", "M:N_ESMP", "M:N_ESMV", "M:N_HELP",
"M:N_SPECIAL", "M:N_SSCCV", "M:N_SSCM", "M:N_SSMA", "M:N_SSMAdvP",
"M:N_SSMI", "M:N_SSMN", "M:N_SSMP", "M:N_SSMV", "M:N_STQ",
"M:N_V", "M:N_nan", "M:Y_CCV", "M:Y_CIN", "M:Y_CLA", "M:Y_CLAdv",
"M:Y_CLN", "M:Y_CLP", "M:Y_CLQ", "M:Y_CLV", "M:Y_CMA1",
"M:Y_CMAdv", "M:Y_CMN1", "M:Y_CMN2", "M:Y_CMN4", "M:Y_CMP",
"M:Y_CMP2", "M:Y_CMV1", "M:Y_CMV2", "M:Y_CMV3",
"M:Y_COMBINATORY", "M:Y_CPA", "M:Y_ESAdvP", "M:Y_ESCCV",
"M:Y_ESCM", "M:Y_ESMA", "M:Y_ESMAdvP", "M:Y_ESMI", "M:Y_ESMN",
"M:Y_ESMP", "M:Y_ESMV", "M:Y_HELP", "M:Y_SPECIAL", "M:Y_SSCCV",
"M:Y_SSCM", "M:Y_SSMA", "M:Y_SSMAdvP", "M:Y_SSMI", "M:Y_SSMN",
"M:Y_SSMP", "M:Y_SSMV", "M:Y_STQ"]
sentence = 'The County Court in Nottingham heard that Roger Gedge, 30, had his leg amputated following the incident outside a rock festival in Wollaton Park, Nottingham, five years ago.'
tokens = SignTaggingTokenizer.tokenize(SignTaggingTokenizer.decode(SignTaggingTokenizer.encode(sentence)))
inputs = SignTaggingTokenizer.encode(sentence, return_tensors="pt")
outputs = SignTaggingModel(inputs)[0]
predictions = torch.argmax(outputs, dim=2)
print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])
======================================================================
| {} | RJ3vans/SignTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | RMRS/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RREXIONN/onetwo | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RRob06/rob_data | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | RTGuo/1st_model | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | RTM/ChatBot | null | [
"conversational",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | null |
# Lucky
| {"tags": ["conversational"]} | RTM/Lucky | null | [
"conversational",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# TIMBOT DialoGPT model | {"tags": ["conversational"]} | RTurk/DialoGPT-small-TIMBOT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | RTurk/DialoGPT-small-harrypotter | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers |
!!!
At the moment, the model is distilled, a version from one of the first checkpoints is available for download.
We plan to post the full model in the next few days.
!!!
This is a distilled HRBert model for an mlm task.
Sentence embeddings can be produced as follows:
```python
# pip install transformers
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model='RabotaRu/HRBert-mini',
tokenizer='RabotaRu/HRBert-mini'
)
fill_mask('<mask> на склад')
``` | {"language": ["ru", "en", "be", "bg", "uk", "ro", "kz", "tg", "tat", "sv", "sl", "sr", "uz", "es", "fi"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm"], "widget": [{"text": "<mask> \u043d\u0430 \u0441\u043a\u043b\u0430\u0434"}]} | RabotaRu/HRBert-mini | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"russian",
"pretraining",
"embeddings",
"masked-lm",
"ru",
"en",
"be",
"bg",
"uk",
"ro",
"kz",
"tg",
"tat",
"sv",
"sl",
"sr",
"uz",
"es",
"fi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers |
### T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with `<hl>` tokens and end the text with `</s>`. For example
`<hl> 42 <hl> is the answer to life, the universe and everything. </s>`
For more deatils see [this](https://github.com/patil-suraj/question_generation) repo.
| {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Although <hl> practicality <hl> beats purity </s>"}]} | Rachneet/t5-base-qg-hl-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Radella/quora_helpful_answer_classifier | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | {} | Radella/quora_helpful_answers_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Radella/quora_helpful_answers_detection | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Radhika0908/Yugasabot_blogs | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Radhika0908/blog | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Radhika0908/blogs | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | RadhikaSatam/CovBert-radhika | null | [
"transformers",
"pytorch",
"jax",
"bert",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | # radical DialoGPT Model | {"tags": ["conversational"]} | Radicalkiddo/DialoGPT-small-Radical | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 14502562
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP", "parameters":{"max_length":1000}}' https://api-inference.huggingface.co/Radvian/autonlp-indo_summarization-14502562
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Radvian/autonlp-data-indo_summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | Radvian/t5_liputan6_finetuned_indonesia_summarization | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:Radvian/autonlp-data-indo_summarization",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
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