modelId
stringlengths
4
112
sha
stringlengths
40
40
lastModified
stringlengths
24
24
tags
sequence
pipeline_tag
stringclasses
29 values
private
bool
1 class
author
stringlengths
2
38
config
null
id
stringlengths
4
112
downloads
float64
0
36.8M
likes
float64
0
712
library_name
stringclasses
17 values
__index_level_0__
int64
0
38.5k
readme
stringlengths
0
186k
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-06_01_05
d34193ddcbe9cf5879c04af7430c70c93b2d8119
2022-03-05T05:01:34.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-06_01_05
59
null
transformers
5,700
Entry not found
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_essays_05_03_2022-06_11_09
2b465a929662f098c4a050c843e6e64f5e64823f
2022-03-05T05:13:43.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_essays_05_03_2022-06_11_09
59
null
transformers
5,701
Entry not found
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_editorials_05_03_2022-06_13_50
87446f91c03aed2c096d6d16e8d6b095f808a221
2022-03-05T05:16:25.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_editorials_05_03_2022-06_13_50
59
null
transformers
5,702
Entry not found
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_webDiscourse_05_03_2022-06_19_06
efac69bdc4889991ad9faf65154f75846bff6f71
2022-03-05T05:21:31.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_webDiscourse_05_03_2022-06_19_06
59
null
transformers
5,703
Entry not found
huggingtweets/baguioni
67dbe15cf7af4773208bcc319822138dd04d6c01
2022-03-27T22:55:21.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/baguioni
59
1
transformers
5,704
--- language: en thumbnail: http://www.huggingtweets.com/baguioni/1648421716784/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1506662013707046914/hVtCPrPL_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">baguio</div> <div style="text-align: center; font-size: 14px;">@baguioni</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from baguio. | Data | baguio | | --- | --- | | Tweets downloaded | 3012 | | Retweets | 1090 | | Short tweets | 527 | | Tweets kept | 1395 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z9nh9v8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baguioni's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s53fr1o/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/baguioni') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tdrenis/finetuned-bot-detector
fb0865bf8e6e889844825e656f8bf252944019b6
2022-04-06T20:31:05.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
tdrenis
null
tdrenis/finetuned-bot-detector
59
null
transformers
5,705
Student project that fine-tuned the roberta-base-openai-detector model on the Twibot-20 dataset.
ArthurZ/opt-350m
5e09c00aa179327fedda07d221042cc7c32f61ab
2022-06-21T20:24:40.000Z
[ "pytorch", "tf", "jax", "opt", "text-generation", "transformers", "license:apache-2.0" ]
text-generation
false
ArthurZ
null
ArthurZ/opt-350m
59
null
transformers
5,706
--- license: apache-2.0 ---
fabiochiu/t5-base-tag-generation
2f6b24e40ad4ec1788e91bfea7de30f42a733609
2022-05-23T13:46:13.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
fabiochiu
null
fabiochiu/t5-base-tag-generation
59
1
transformers
5,707
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-tag-generation results: [] widget: - text: "Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected." example_title: "Programming" --- # Model description This model is [t5-base](https://huggingface.co/t5-base) fine-tuned on the [190k Medium Articles](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) dataset for predicting article tags using the article textual content as input. While usually formulated as a multi-label classification problem, this model deals with _tag generation_ as a text2text generation task (inspiration from [text2tags](https://huggingface.co/efederici/text2tags)). # How to use the model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import nltk nltk.download('punkt') tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation") model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation") text = """ Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected. """ inputs = tokenizer([text], max_length=512, truncation=True, return_tensors="pt") output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64) decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0] tags = list(set(decoded_output.strip().split(", "))) print(tags) # ['Programming', 'Code', 'Software Development', 'Programming Languages', # 'Software', 'Developer', 'Python', 'Software Engineering', 'Science', # 'Engineering', 'Technology', 'Computer Science', 'Coding', 'Digital', 'Tech', # 'Python Programming'] ``` ## Data cleaning The dataset is composed of Medium articles and their tags. However, each Medium article can have at most five tags, therefore the author needs to choose what he/she believes are the best tags (mainly for SEO-related purposes). This means that an article with the "Python" tag may have not the "Programming Languages" tag, even though the first implies the latter. To clean the dataset accounting for this problem, a hand-made taxonomy of about 1000 tags was built. Using the taxonomy, the tags of each articles have been augmented (e.g. an article with the "Python" tag will have the "Programming Languages" tag as well, as the taxonomy says that "Python" is part of "Programming Languages"). The taxonomy is not public, if you are interested in it please send an email at [email protected]. ## Training and evaluation data The model has been trained on a single epoch spanning about 50000 articles, evaluating on 1000 random articles not used during training. ## Evaluation results - eval_loss: 0.8474 - eval_rouge1: 38.6033 - eval_rouge2: 20.5952 - eval_rougeL: 36.4458 - eval_rougeLsum: 36.3202 - eval_gen_len: 15.257 # average number of generated tokens ## Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
autoevaluate/multi-class-classification
79771c00c9552278050dc1ef39d694ad71d51fbc
2022-06-02T12:25:30.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
autoevaluate
null
autoevaluate/multi-class-classification
59
null
transformers
5,708
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: multi-class-classification results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.928 - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - name: Accuracy type: accuracy value: 0.9185 verified: true - name: Precision Macro type: precision value: 0.8738350796775306 verified: true - name: Precision Micro type: precision value: 0.9185 verified: true - name: Precision Weighted type: precision value: 0.9179425177997311 verified: true - name: Recall Macro type: recall value: 0.8650962919021573 verified: true - name: Recall Micro type: recall value: 0.9185 verified: true - name: Recall Weighted type: recall value: 0.9185 verified: true - name: F1 Macro type: f1 value: 0.8692821860210945 verified: true - name: F1 Micro type: f1 value: 0.9185 verified: true - name: F1 Weighted type: f1 value: 0.9181177508591364 verified: true - name: loss type: loss value: 0.20905950665473938 verified: true - name: matthews_correlation type: matthews_correlation value: 0.8920254536671932 verified: true --- <!-- 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. --> # multi-class-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2009 - Accuracy: 0.928 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2643 | 1.0 | 1000 | 0.2009 | 0.928 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/arstechnica
b341e91e121507278f4c54880fad71ac8e687c14
2022-06-20T06:05:42.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/arstechnica
59
null
transformers
5,709
--- language: en thumbnail: http://www.huggingtweets.com/arstechnica/1655705137296/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/2215576731/ars-logo_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ars Technica</div> <div style="text-align: center; font-size: 14px;">@arstechnica</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ars Technica. | Data | Ars Technica | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 27 | | Short tweets | 0 | | Tweets kept | 3223 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2n328dqy/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @arstechnica's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/koacg5oh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/koacg5oh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/arstechnica') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
fujiki/t5-efficient-xl-nl12-en2ja
5099abd04699a806c353e3f276f09c47e4aa9b31
2022-07-04T02:18:16.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]
text2text-generation
false
fujiki
null
fujiki/t5-efficient-xl-nl12-en2ja
59
null
transformers
5,710
--- license: afl-3.0 ---
ckb/toki-en-mt
ad3e608a405d5c5c96f3b3cd5561b19efab102d9
2022-07-09T10:57:35.000Z
[ "pytorch", "marian", "text2text-generation", "tok", "en", "transformers", "generated_from_trainer", "translation", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
ckb
null
ckb/toki-en-mt
59
null
transformers
5,711
--- license: apache-2.0 language: - tok - en tags: - generated_from_trainer - translation metrics: - bleu model-index: - name: toki-en-mt results: [] widget: - text: "toki! mi jan Ton. mi lon ma Tawan." - text: "soweli li toki ala toki e toki Inli?" --- <!-- 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. --> # toki-en-mt This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ROMANCE-en](https://huggingface.co/Helsinki-NLP/opus-mt-ROMANCE-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2840 - Bleu: 26.7612 - Gen Len: 9.0631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.7228 | 1.0 | 1260 | 1.4572 | 19.9464 | 9.2177 | | 1.3182 | 2.0 | 2520 | 1.3356 | 22.4628 | 9.1263 | | 1.1241 | 3.0 | 3780 | 1.3028 | 23.5152 | 9.0462 | | 0.9995 | 4.0 | 5040 | 1.2784 | 23.9526 | 9.1697 | | 0.8945 | 5.0 | 6300 | 1.2739 | 24.7707 | 9.0914 | | 0.8331 | 6.0 | 7560 | 1.2725 | 25.3477 | 9.0518 | | 0.7641 | 7.0 | 8820 | 1.2770 | 26.165 | 9.0245 | | 0.7163 | 8.0 | 10080 | 1.2809 | 25.8053 | 9.0933 | | 0.6886 | 9.0 | 11340 | 1.2799 | 26.5752 | 9.0669 | | 0.6627 | 10.0 | 12600 | 1.2840 | 26.7612 | 9.0631 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
p-christ/QandAClassifier
a4a4bbaea9defc398dc588b2ecf958cfb4bf4e8c
2022-07-11T16:49:51.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
p-christ
null
p-christ/QandAClassifier
59
null
transformers
5,712
Entry not found
OATML-Markslab/Tranception
d1ef627127760862bc0615ee74372ad4b23b2193
2022-07-18T15:25:35.000Z
[ "pytorch", "tranception", "fill-mask", "arxiv:2205.13760", "transformers", "autotrain_compatible" ]
fill-mask
false
OATML-Markslab
null
OATML-Markslab/Tranception
59
3
transformers
5,713
# Tranception model This Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper ["Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval"](https://arxiv.org/abs/2205.13760). The official GitHub repository can be accessed [here](https://github.com/OATML-Markslab/Tranception). This project is a joint collaboration between the [Marks lab](https://www.deboramarkslab.com/) and the [OATML group](https://oatml.cs.ox.ac.uk/). ## Abstract The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap. We introduce Tranception, a novel transformer architecture leveraging autoregressive predictions and retrieval of homologous sequences at inference to achieve state-of-the-art fitness prediction performance. Given its markedly higher performance on multiple mutants, robustness to shallow alignments and ability to score indels, our approach offers significant gain of scope over existing approaches. To enable more rigorous model testing across a broader range of protein families, we develop ProteinGym -- an extensive set of multiplexed assays of variant effects, substantially increasing both the number and diversity of assays compared to existing benchmarks. ## License This project is available under the MIT license. ## Reference If you use Tranception or other files provided through our GitHub repository, please cite the following paper: ``` Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. ``` ## Links Pre-print: https://arxiv.org/abs/2205.13760 GitHub: https://github.com/OATML-Markslab/Tranception
eclat12450/fine-tuned-NSPbert-12
6f0e086fb7fcff6415008a989bb1f57177c45708
2022-07-20T10:14:54.000Z
[ "pytorch", "bert", "next-sentence-prediction", "transformers" ]
null
false
eclat12450
null
eclat12450/fine-tuned-NSPbert-12
59
null
transformers
5,714
Entry not found
trickstters/DialoGPT-small-evanbot
fc7149e47717f2b9a0d58356d93be12c37c7db5b
2022-07-27T09:01:01.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
trickstters
null
trickstters/DialoGPT-small-evanbot
59
null
transformers
5,715
--- tags: - conversational --- # a
CalvinHuang/mt5-small-finetuned-amazon-en-es
b240a4a8723a1dcd7e8b4438341f7fd8c3e1bae4
2022-02-02T03:50:37.000Z
[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "summarization", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
summarization
false
CalvinHuang
null
CalvinHuang/mt5-small-finetuned-amazon-en-es
58
1
transformers
5,716
--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0393 - Rouge1: 17.2936 - Rouge2: 8.0678 - Rougel: 16.8129 - Rougelsum: 16.9991 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.6665 | 1.0 | 1209 | 3.2917 | 13.912 | 5.595 | 13.2984 | 13.4171 | | 3.8961 | 2.0 | 2418 | 3.1711 | 16.2845 | 8.6033 | 15.5509 | 15.7383 | | 3.5801 | 3.0 | 3627 | 3.0917 | 17.316 | 8.122 | 16.697 | 16.773 | | 3.4258 | 4.0 | 4836 | 3.0583 | 16.1347 | 7.7829 | 15.6475 | 15.7804 | | 3.3154 | 5.0 | 6045 | 3.0573 | 17.5918 | 8.7349 | 17.0537 | 17.2216 | | 3.2438 | 6.0 | 7254 | 3.0479 | 17.2294 | 8.0383 | 16.8141 | 16.9858 | | 3.2024 | 7.0 | 8463 | 3.0377 | 17.2918 | 8.139 | 16.8178 | 16.9671 | | 3.1745 | 8.0 | 9672 | 3.0393 | 17.2936 | 8.0678 | 16.8129 | 16.9991 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
DarshanDeshpande/marathi-distilbert
b0fe751059df2cb52a8dd72a2aa5c4c346e2809a
2021-03-23T08:20:29.000Z
[ "pytorch", "tf", "distilbert", "fill-mask", "mr", "dataset:Oscar Corpus, News, Stories", "arxiv:1910.01108", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
DarshanDeshpande
null
DarshanDeshpande/marathi-distilbert
58
3
transformers
5,717
--- language: - mr tags: - fill-mask license: apache-2.0 datasets: - Oscar Corpus, News, Stories widget: - text: "हा खरोखर चांगला [MASK] आहे." --- # Marathi DistilBERT ## Model description This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. ``` DISCLAIMER This model has not been thoroughly tested and may contain biased opinions or inappropriate language. User discretion is advised ``` ## Training data The training data has been extracted from a variety of sources, mainly including: 1. Oscar Corpus 2. Marathi Newspapers 3. Marathi storybooks and articles The data is cleaned by removing all languages other than Marathi, while preserving common punctuations ## Training procedure The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%. ## Example ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="DarshanDeshpande/marathi-distilbert", tokenizer="DarshanDeshpande/marathi-distilbert", ) fill_mask("हा खरोखर चांगला [MASK] आहे.") ``` ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <h3>Authors </h3> <h5>1. Darshan Deshpande: <a href="https://github.com/DarshanDeshpande">GitHub</a>, <a href="https://www.linkedin.com/in/darshan-deshpande/">LinkedIn</a><h5> <h5>2. Harshavardhan Abichandani: <a href="https://github.com/Baras64">GitHub</a>, <a href="http://​www.linkedin.com/in/harsh-abhi">LinkedIn</a><h5>
Helsinki-NLP/opus-mt-en-rw
6cd0e5c91a2e1a06838ab3eae0d8fc45d257a2d9
2021-09-09T21:38:55.000Z
[ "pytorch", "marian", "text2text-generation", "en", "rw", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-rw
58
null
transformers
5,718
--- tags: - translation license: apache-2.0 --- ### opus-mt-en-rw * source languages: en * target languages: rw * OPUS readme: [en-rw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-rw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-rw/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rw/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rw/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.rw | 33.3 | 0.569 | | Tatoeba.en.rw | 13.8 | 0.503 |
IlyaGusev/sber_rut5_filler
c7f5e2b9cbb3937f2632f827364b8e922f788e42
2022-07-13T15:34:32.000Z
[ "pytorch", "t5", "text2text-generation", "ru", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
IlyaGusev
null
IlyaGusev/sber_rut5_filler
58
1
transformers
5,719
--- language: - ru license: apache-2.0 widget: - text: Эта блядь меня заебала</s> Эта <extra_id_0> меня <extra_id_1> ---
ankur310794/bart-base-keyphrase-generation-openkp
92001c83e3f1533ca560f083cf38db2a2390fd11
2021-04-09T08:43:55.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ankur310794
null
ankur310794/bart-base-keyphrase-generation-openkp
58
null
transformers
5,720
Entry not found
danghuy1999/gpt2-viwiki
c1e1934242d48aabd63b2f9ce4d5aa025ecf7c27
2021-08-08T17:59:19.000Z
[ "pytorch", "tf", "gpt2", "vi", "transformers", "gpt2-viwiki", "license:mit" ]
null
false
danghuy1999
null
danghuy1999/gpt2-viwiki
58
1
transformers
5,721
--- language: vi tags: - gpt2-viwiki license: mit --- # GPT-2 Fine-tuning in Vietnamese Wikipedia ## Model description This is a Vietnamese GPT-2 model which is finetuned on the [Latest pages articles of Vietnamese Wikipedia](https://dumps.wikimedia.org/viwiki/latest/viwiki-latest-pages-articles.xml.bz2). ## Dataset The dataset is about 800MB, includes many articles from Wikipedia. ## How to use You can use this model to: - Tokenize Vietnamese sentences with GPT2Tokenizer. - Generate text seems like a Wikipedia article. - Finetune it to other downstream tasks. Here is how to use the model to generate text in Pytorch: ```python import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('danghuy1999/gpt2-viwiki') model = GPT2LMHeadModel.from_pretrained('danghuy1999/gpt2-viwiki').to('cuda') text = "Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử" input_ids = tokenizer.encode(text, return_tensors='pt').to('cuda') max_length = 100 sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id, do_sample=True, max_length=max_length, min_length=max_length, top_k=40, num_beams=5, early_stopping=True, no_repeat_ngram_size=2, num_return_sequences=3) for i, sample_output in enumerate(sample_outputs): print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist()))) print('\n---') ``` And the results are: ```bash >> Generated text 1 Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử. Mặc dù thuyết tương đối tổng quát không được áp dụng rộng rãi trong nhiều lĩnh vực khác nhau, nhưng các nhà lý thuyết đã đưa ra khái niệm rộng hơn về tính chất của vật chất. Một trong những nghiên cứu của Albert Einstein về sự tồn tại của hệ quy chiếu quán tính, ông đã đề xuất rằng một lực hấp dẫn có thể có khối lượng bằng năng lượng của nó. Tuy nhiên, những người cho rằng --- >> Generated text 2 Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử. Tuy nhiên, thuyết tương đối hẹp không phải là lý thuyết của Einstein. Cho đến tận cuối thế kỷ 19, Albert Einstein đã chứng minh được sự tồn tại của lực hấp dẫn trong một số trường hợp đặc biệt. Năm 1915, ông đưa ra khái niệm "khối lượng" để miêu tả chuyển động lượng của một hạt bằng khối lượng nghỉ của nó. Ông cho rằng năng lượng "m" là một thành phần của --- >> Generated text 3 Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử. Tuy nhiên, thuyết tương đối hẹp không được chấp nhận rộng rãi bởi các nhà lý thuyết. Một trong những nghiên cứu của Einstein về tính chất của lực hấp dẫn là vào năm 1905, ông đã đưa ra một khái niệm về lực học. Ông đã phát biểu rằng nếu một hạt mang điện tích dương, nó có thể chuyển đổi năng lượng của nó thành các hạt khác. Năm 1915, Arthur Eddington phát minh ra --- ``` You can do the same with **Tensorflow** by using the model **TFGPT2Tokenizer** instead.
educhav/J-DialoGPT-small
5850d899162791adedf231186fe7a4c1580a21bb
2022-01-22T02:35:11.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
educhav
null
educhav/J-DialoGPT-small
58
null
transformers
5,722
--- tags: - conversational --- # J Cole Patt
ethzanalytics/ai-msgbot-gpt2-XL-dialogue
91b073bed82d78b0149d5e59fca58d07f99188f1
2022-02-22T15:46:24.000Z
[ "pytorch", "gpt2", "text-generation", "en", "dataset:natural questions", "transformers", "gpt", "license:mit" ]
text-generation
false
ethzanalytics
null
ethzanalytics/ai-msgbot-gpt2-XL-dialogue
58
1
transformers
5,723
--- language: - en tags: - text-generation - gpt2 - gpt license: mit datasets: - natural questions widget: - text: "Do you like my new haircut?\nperson beta:\n\n" example_title: "haircut" - text: "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n" example_title: "teaching" - text: "What's your favorite animal? Mine is the dog? \nperson beta:\n\n" example_title: "favorite" - text: "how much does it cost?\nperson beta:\n\n" example_title: "money" inference: parameters: min_length: 2 max_length: 64 length_penalty: 0.6 no_repeat_ngram_size: 3 do_sample: True top_p: 0.85 top_k: 10 repetition_penalty: 2.1 --- # ai-msgbot GPT2-XL-dialogue _NOTE: model card is WIP_ GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`. The resulting model was then **further fine-tuned** on the [Daily Dialogues](http://yanran.li/dailydialog) for 40k steps, with **34**/36 layers frozen. Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ended chatbot (of course, if other use cases arise, have at it). ## conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` ## examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` into the prompt text the model is forced to respond to instead of adding onto the entered prompt. ## citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)}, year={2019}, } @inproceedings{li-etal-2017-dailydialog, title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset", author = "Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi", booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2017", address = "Taipei, Taiwan", publisher = "Asian Federation of Natural Language Processing", url = "https://aclanthology.org/I17-1099", pages = "986--995", abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}", } ```
funnel-transformer/large-base
cebf5df5b78c98badc256dfdf302d9403fd08bae
2020-12-11T21:40:28.000Z
[ "pytorch", "tf", "funnel", "feature-extraction", "en", "dataset:bookcorpus", "dataset:wikipedia", "dataset:gigaword", "arxiv:2006.03236", "transformers", "license:apache-2.0" ]
feature-extraction
false
funnel-transformer
null
funnel-transformer/large-base
58
null
transformers
5,724
--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer large model (B8-8-8 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw 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, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `large` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base") 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 FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
huggingtweets/destiny_thememe
a87042566e8e9c010f3d80f1d54f97eda0fcee43
2021-05-22T01:27:17.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/destiny_thememe
58
null
transformers
5,725
--- language: en thumbnail: https://www.huggingtweets.com/destiny_thememe/1616803427645/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372396296699404291/SySu1wAp_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">♦️Moira Perfected♦️ 🤖 AI Bot </div> <div style="font-size: 15px">@destiny_thememe bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@destiny_thememe's tweets](https://twitter.com/destiny_thememe). | Data | Quantity | | --- | --- | | Tweets downloaded | 3242 | | Retweets | 186 | | Short tweets | 772 | | Tweets kept | 2284 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bbkix40/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @destiny_thememe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20xpitr1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20xpitr1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/destiny_thememe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/muratpak
5b92089b726101c723bb60dd38647a1f057b085c
2021-10-18T17:22:31.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/muratpak
58
null
transformers
5,726
--- language: en thumbnail: https://www.huggingtweets.com/muratpak/1634577747584/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1442159742558765064/RFB5JjIk_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Pak</div> <div style="text-align: center; font-size: 14px;">@muratpak</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Pak. | Data | Pak | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 686 | | Short tweets | 964 | | Tweets kept | 1600 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1s58abff/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @muratpak's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30zzcgkm) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30zzcgkm/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/muratpak') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
joeddav/distilbert-base-uncased-agnews-student
41503004a8bc8c31721fc37a384f406544862d97
2021-02-18T20:41:19.000Z
[ "pytorch", "tf", "distilbert", "text-classification", "en", "dataset:ag_news", "transformers", "tensorflow", "license:mit" ]
text-classification
false
joeddav
null
joeddav/distilbert-base-uncased-agnews-student
58
null
transformers
5,727
--- 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.
jonatasgrosman/paraphrase
73b6aede4e8f82dddd37fd14f2124e9088c94356
2021-05-23T06:01:26.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
jonatasgrosman
null
jonatasgrosman/paraphrase
58
null
transformers
5,728
testing
m3hrdadfi/xlmr-large-qa-fa
81c1e240f5b2def9e1db967d23693e4182a8b556
2021-10-12T08:36:53.000Z
[ "pytorch", "tf", "xlm-roberta", "question-answering", "fa", "multilingual", "dataset:SajjadAyoubi/persian_qa", "transformers", "roberta", "squad", "model-index", "autotrain_compatible" ]
question-answering
false
m3hrdadfi
null
m3hrdadfi/xlmr-large-qa-fa
58
null
transformers
5,729
--- language: - fa - multilingual tags: - question-answering - xlm-roberta - roberta - squad datasets: - SajjadAyoubi/persian_qa metrics: - squad_v2 widget: - text: "کاربردهای لاپلاسین؟" context: "معادلهٔ لاپلاس یک معادله دیفرانسیل با مشتقات جزئی است که از اهمّیّت و کاربرد فراوانی در ریاضیّات، فیزیک، و مهندسی برخوردار است. به عنوان چند نمونه می‌شود به زمینه‌هایی همچون الکترومغناطیس، ستاره‌شناسی، و دینامیک سیالات اشاره کرد که حلّ این معادله در آن‌ها کاربرد دارد." - text: "نام دیگر شب یلدا؟" context: "شب یَلدا یا شب چلّه یکی از کهن‌ترین جشن‌های ایرانی است. در این جشن، طی شدن بلندترین شب سال و به دنبال آن بلندتر شدن طول روزها در نیم‌کرهٔ شمالی، که مصادف با انقلاب زمستانی است، گرامی داشته می‌شود. نام دیگر این شب «چِلّه» است، زیرا برگزاری این جشن، یک آیین ایرانی‌است." - text: "کهن ترین جشن ایرانی‌ها چه است؟" context: "شب یَلدا یا شب چلّه یکی از کهن‌ترین جشن‌های ایرانی است. در این جشن، طی شدن بلندترین شب سال و به دنبال آن بلندتر شدن طول روزها در نیم‌کرهٔ شمالی، که مصادف با انقلاب زمستانی است، گرامی داشته می‌شود. نام دیگر این شب «چِلّه» است، زیرا برگزاری این جشن، یک آیین ایرانی‌است." - text: "شب یلدا مصادف با چه پدیده‌ای است؟" context: "شب یَلدا یا شب چلّه یکی از کهن‌ترین جشن‌های ایرانی است. در این جشن، طی شدن بلندترین شب سال و به دنبال آن بلندتر شدن طول روزها در نیم‌کرهٔ شمالی، که مصادف با انقلاب زمستانی است، گرامی داشته می‌شود. نام دیگر این شب «چِلّه» است، زیرا برگزاری این جشن، یک آیین ایرانی‌است." model-index: - name: XLM-RoBERTa large for QA (PersianQA - 🇮🇷) results: - task: type: question-answering name: Question Answering dataset: type: SajjadAyoubi/persian_qa name: PersianQA args: fa metrics: - type: squad_v2 value: 83.46 name: Eval F1 args: max_order - type: squad_v2 value: 66.88 name: Eval Exact args: max_order --- # XLM-RoBERTa large for QA (PersianQA - 🇮🇷) This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the [PersianQA](https://github.com/sajjjadayobi/PersianQA) dataset. ## Hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20.0 - mixed_precision_training: Native AMP ## Performance Evaluation results on the eval set with the official [eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ### Evalset ```text "HasAns_exact": 58.678955453149, "HasAns_f1": 82.3746683591845, "HasAns_total": 651, "NoAns_exact": 86.02150537634408, "NoAns_f1": 86.02150537634408, "NoAns_total": 279, "exact": 66.88172043010752, "f1": 83.46871946433232, "total": 930 ``` ## Usage ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name_or_path = "m3hrdadfi/xlmr-large-qa-fa" nlp = pipeline('question-answering', model=model_name_or_path, tokenizer=model_name_or_path) context = """ شب یَلدا یا شب چلّه یکی از کهن‌ترین جشن‌های ایرانی است. در این جشن، طی شدن بلندترین شب سال و به دنبال آن بلندتر شدن طول روزها در نیم‌کرهٔ شمالی، که مصادف با انقلاب زمستانی است، گرامی داشته می‌شود. نام دیگر این شب «چِلّه» است، زیرا برگزاری این جشن، یک آیین ایرانی‌است. """ # Translation [EN] # context = [ # Yalda night or Cheleh night is one of the oldest Iranian celebrations. # The festival celebrates the longest night of the year, followed by longer days in the Northern Hemisphere, # which coincides with the Winter Revolution. # Another name for this night is "Chelleh", because holding this celebration is an Iranian ritual. # ] questions = [ "نام دیگر شب یلدا؟", "کهن ترین جشن ایرانی‌ها چه است؟", "شب یلدا مصادف با چه پدیده‌ای است؟" ] # Translation [EN] # questions = [ # Another name for Yalda night? # What is the ancient tradition of Iranian celebration? # What phenomenon does Yalda night coincide with? # ] kwargs = {} for question in questions: r = nlp(question=question, context=context, **kwargs) answer = " ".join([token.strip() for token in r["answer"].strip().split() if token.strip()]) print(f"{question} {answer}") ``` **Output** ```text نام دیگر شب یلدا؟ «چِلّه» کهن ترین جشن ایرانی‌ها چه است؟ شب یَلدا یا شب چلّه شب یلدا مصادف با چه پدیده‌ای است؟ انقلاب زمستانی # Translation [EN] # Another name for Yalda night? Cheleh night # What is the ancient tradition of Iranian celebration? Yalda night or Chele night # What phenomenon does Yalda night coincide with? Winter revolution ``` ## Authors - [Mehrdad Farahani](https://github.com/m3hrdadfi) ## Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.9.1+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
neuralspace-reverie/indic-transformers-bn-roberta
3ba101b2b34f7a2af1c5595f1d4be6598e692e5b
2021-05-20T18:47:17.000Z
[ "pytorch", "tf", "jax", "roberta", "fill-mask", "bn", "transformers", "MaskedLM", "Bengali", "RoBERTa", "Question-Answering", "Token Classification", "Text Classification", "autotrain_compatible" ]
fill-mask
false
neuralspace-reverie
null
neuralspace-reverie/indic-transformers-bn-roberta
58
null
transformers
5,730
--- language: - bn tags: - MaskedLM - Bengali - RoBERTa - Question-Answering - Token Classification - Text Classification --- # Indic-Transformers Bengali RoBERTa ## Model description This is a RoBERTa language model pre-trained on ~6 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/). This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training. ## Intended uses & limitations #### How to use ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('neuralspace-reverie/indic-transformers-bn-roberta') model = AutoModel.from_pretrained('neuralspace-reverie/indic-transformers-bn-roberta') text = "আপনি কেমন আছেন?" input_ids = tokenizer(text, return_tensors='pt')['input_ids'] out = model(input_ids)[0] print(out.shape) # out = [1, 10, 768] ``` #### Limitations and bias The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
salesken/text_generate
e11e6a9123081a2f1e467b8dca90930c50d7c265
2021-05-23T12:38:21.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers", "salesken" ]
text-generation
false
salesken
null
salesken/text_generate
58
1
transformers
5,731
--- tags: salesken widget: - text: "Which name is also used to describe the Amazon rainforest in English? " --- ```python from transformers import AutoTokenizer, AutoModelWithLMHead import torch if torch.cuda.is_available(): device = torch.device("cuda") else : device = "cpu" tokenizer = AutoTokenizer.from_pretrained("salesken/text_generate") model = AutoModelWithLMHead.from_pretrained("salesken/text_generate").to(device) input_query="tough challenges make you stronger. " input_ids = tokenizer.encode(input_query.lower(), return_tensors='pt').to(device) sample_outputs = model.generate(input_ids, do_sample=True, num_beams=1, max_length=1024, temperature=0.99, top_k = 10, num_return_sequences=1) for i in range(len(sample_outputs)): print(tokenizer.decode(sample_outputs[i], skip_special_tokens=True)) ```
ykacer/bert-base-cased-imdb-sequence-classification
05012cac18df27f33acd2c512f16a48542108c2f
2021-05-20T09:31:37.000Z
[ "pytorch", "jax", "bert", "text-classification", "en", "dataset:imdb", "transformers", "sequence", "classification", "license:apache-2.0" ]
text-classification
false
ykacer
null
ykacer/bert-base-cased-imdb-sequence-classification
58
null
transformers
5,732
--- language: - en thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png tags: - sequence - classification license: apache-2.0 datasets: - imdb metrics: - accuracy ---
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_48_17
d5e6f66dc9c1a2f9ce82e32b176a1c642d284878
2022-03-05T04:50:54.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_48_17
58
null
transformers
5,733
Entry not found
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_53_50
0e2c93d656a0817944f32b15c541813f518169ee
2022-03-05T04:56:24.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_53_50
58
null
transformers
5,734
Entry not found
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_webDiscourse_05_03_2022-06_08_23
563420fe1d72c7a8d9968a9ec3ce1bbe805aa384
2022-03-05T05:11:02.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_webDiscourse_05_03_2022-06_08_23
58
null
transformers
5,735
Entry not found
l3cube-pune/marathi-ner
7cec0d26b321405a2da5415fc01314a5fafe9eb5
2022-06-26T15:15:30.000Z
[ "pytorch", "bert", "token-classification", "mr", "dataset:L3Cube-MahaNER", "arxiv:2204.06029", "transformers", "license:cc-by-4.0", "autotrain_compatible" ]
token-classification
false
l3cube-pune
null
l3cube-pune/marathi-ner
58
null
transformers
5,736
--- language: mr tags: license: cc-by-4.0 datasets: - L3Cube-MahaNER widget: - text: "I like you. </s></s> I love you." --- ## MahaNER-BERT MahaNER-BERT is a MahaBERT(l3cube-pune/marathi-bert) model fine-tuned on L3Cube-MahaNER - a Marathi named entity recognition dataset. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.06029) ``` @InProceedings{litake-EtAl:2022:WILDRE6, author = {Litake, Onkar and Sabane, Maithili Ravindra and Patil, Parth Sachin and Ranade, Aparna Abhijeet and Joshi, Raviraj}, title = {L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models}, booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {29--34} } ```
wanyu/IteraTeR-BART-Revision-Generator
b4fd9ef8529abba0610b1d73c56d7cdb5143d70f
2022-04-04T20:09:49.000Z
[ "pytorch", "bart", "text2text-generation", "dataset:IteraTeR_full_sent", "arxiv:2203.03802", "transformers", "autotrain_compatible" ]
text2text-generation
false
wanyu
null
wanyu/IteraTeR-BART-Revision-Generator
58
null
transformers
5,737
--- datasets: - IteraTeR_full_sent --- # IteraTeR BART model This model was obtained by fine-tuning [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset. Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br> Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang ## Text Revision Task Given an edit intention and an original sentence, our model can generate a revised sentence.<br> The edit intentions are provided by [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset, which are categorized as follows: <table> <tr> <th>Edit Intention</th> <th>Definition</th> <th>Example</th> </tr> <tr> <td>clarity</td> <td>Make the text more formal, concise, readable and understandable.</td> <td> Original: It's like a house which anyone can enter in it. <br> Revised: It's like a house which anyone can enter. </td> </tr> <tr> <td>fluency</td> <td>Fix grammatical errors in the text.</td> <td> Original: In the same year he became the Fellow of the Royal Society. <br> Revised: In the same year, he became the Fellow of the Royal Society. </td> </tr> <tr> <td>coherence</td> <td>Make the text more cohesive, logically linked and consistent as a whole.</td> <td> Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br> Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. </td> </tr> <tr> <td>style</td> <td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td> <td> Original: She was last seen on 2005-10-22. <br> Revised: She was last seen on October 22, 2005. </td> </tr> </table> ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator") model = AutoModelForSeq2SeqLM.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator") before_input = '<fluency> I likes coffee.' model_input = tokenizer(before_input, return_tensors='pt') model_outputs = model.generate(**model_input, num_beams=8, max_length=1024) after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0] ```
arghya007/roberta-scarcasm-discriminator
7e3e92ff796c499b68daefff45369064dd3f95b2
2022-04-10T17:42:26.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
arghya007
null
arghya007/roberta-scarcasm-discriminator
58
null
transformers
5,738
Entry not found
Helsinki-NLP/opus-mt-tc-big-en-ces_slk
0850dc863d8df39712a09a8068d23a3acca4fb17
2022-06-01T13:03:14.000Z
[ "pytorch", "marian", "text2text-generation", "ces", "slk", "cs", "sk", "en", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tc-big-en-ces_slk
58
null
transformers
5,739
--- language: - ces - slk - cs - sk - en tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-en-ces_slk results: - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: flores101-devtest type: flores_101 args: eng ces devtest metrics: - name: BLEU type: bleu value: 34.1 - task: name: Translation eng-slk type: translation args: eng-slk dataset: name: flores101-devtest type: flores_101 args: eng slk devtest metrics: - name: BLEU type: bleu value: 35.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: eng-ces metrics: - name: BLEU type: bleu value: 33.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: eng-ces metrics: - name: BLEU type: bleu value: 33.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: news-test2008 type: news-test2008 args: eng-ces metrics: - name: BLEU type: bleu value: 22.8 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-ces metrics: - name: BLEU type: bleu value: 47.5 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2009 type: wmt-2009-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.3 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2010 type: wmt-2010-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2011 type: wmt-2011-news args: eng-ces metrics: - name: BLEU type: bleu value: 25.5 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2012 type: wmt-2012-news args: eng-ces metrics: - name: BLEU type: bleu value: 22.6 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2013 type: wmt-2013-news args: eng-ces metrics: - name: BLEU type: bleu value: 27.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2014 type: wmt-2014-news args: eng-ces metrics: - name: BLEU type: bleu value: 31.4 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2015 type: wmt-2015-news args: eng-ces metrics: - name: BLEU type: bleu value: 27.0 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2016 type: wmt-2016-news args: eng-ces metrics: - name: BLEU type: bleu value: 29.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2017 type: wmt-2017-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.9 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2018 type: wmt-2018-news args: eng-ces metrics: - name: BLEU type: bleu value: 24.6 - task: name: Translation eng-ces type: translation args: eng-ces dataset: name: newstest2019 type: wmt-2019-news args: eng-ces metrics: - name: BLEU type: bleu value: 26.4 --- # opus-mt-tc-big-en-ces_slk Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-13 * source language(s): eng * target language(s): ces * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-ces+slk README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ces+slk/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ces<< We were enemies.", ">>ces<< Do you think Tom knows what's going on?" ] model_name = "pytorch-models/opus-mt-tc-big-en-ces_slk" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Byli jsme nepřátelé. # Myslíš, že Tom ví, co se děje? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ces_slk") print(pipe(">>ces<< We were enemies.")) # expected output: Byli jsme nepřátelé. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-ces | tatoeba-test-v2021-08-07 | 0.66128 | 47.5 | 13824 | 91332 | | eng-ces | flores101-devtest | 0.60411 | 34.1 | 1012 | 22101 | | eng-slk | flores101-devtest | 0.62415 | 35.9 | 1012 | 22543 | | eng-ces | multi30k_test_2016_flickr | 0.58547 | 33.4 | 1000 | 10503 | | eng-ces | multi30k_test_2018_flickr | 0.59236 | 33.4 | 1071 | 11631 | | eng-ces | newssyscomb2009 | 0.52702 | 25.3 | 502 | 10032 | | eng-ces | news-test2008 | 0.50286 | 22.8 | 2051 | 42484 | | eng-ces | newstest2009 | 0.52152 | 24.3 | 2525 | 55533 | | eng-ces | newstest2010 | 0.52527 | 24.4 | 2489 | 52955 | | eng-ces | newstest2011 | 0.52721 | 25.5 | 3003 | 65653 | | eng-ces | newstest2012 | 0.50007 | 22.6 | 3003 | 65456 | | eng-ces | newstest2013 | 0.53643 | 27.4 | 3000 | 57250 | | eng-ces | newstest2014 | 0.58944 | 31.4 | 3003 | 59902 | | eng-ces | newstest2015 | 0.55094 | 27.0 | 2656 | 45858 | | eng-ces | newstest2016 | 0.56864 | 29.9 | 2999 | 56998 | | eng-ces | newstest2017 | 0.52504 | 24.9 | 3005 | 54361 | | eng-ces | newstest2018 | 0.52490 | 24.6 | 2983 | 54652 | | eng-ces | newstest2019 | 0.53994 | 26.4 | 1997 | 43113 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 16:46:48 EEST 2022 * port machine: LM0-400-22516.local
charityking2358/taglish-electra
566e2edf3c9dcc7755d91f73df3e5d51944d6c74
2022-04-26T02:19:48.000Z
[ "pytorch", "transformers" ]
null
false
charityking2358
null
charityking2358/taglish-electra
58
null
transformers
5,740
## Taglish-Electra Our Taglish-Electra model was pretrained with two Filipino training datasets and one English dataset to increase improvement against Filipino text with English where speakers may code-switch between the two languages. 1) Openwebtext (English) 2) WikiText-TL-39 (Filipino) 3) [TLUnified Large Scale Corpus](https://www.blaisecruz.com/resources/) This is the discriminator model, which is the main Transformer used for finetuning to downstream tasks. For generation, mask-filling, and retraining, refer to the Generator models.
Jackett/detr_test
829cf95c562b6f28dc31c89e897c7aeb9270137a
2022-05-17T07:54:37.000Z
[ "pytorch", "detr", "object-detection", "transformers" ]
object-detection
false
Jackett
null
Jackett/detr_test
58
null
transformers
5,741
Entry not found
smc/PANDA_ConvNeXT
d5d34bfd500b5e013766f4eae4933cb4371ce3fa
2022-05-25T20:19:25.000Z
[ "pytorch", "convnext", "image-classification", "transformers", "model-index" ]
image-classification
false
smc
null
smc/PANDA_ConvNeXT
58
1
transformers
5,742
--- tags: - image-classification - pytorch metrics: - accuracy - Cohen's Kappa model-index: - name: PANDA_ConvNeXT results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5491307377815247 - name: Quadratic Cohen's Kappa type: Quadratic Cohen's Kappa value: 0.6630877256393433 --- # PANDA_ConvNeXT An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input Example Images (1152,1152,3) 36 WSI patches: ISUP 0: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c02d3bb3a62519b31c63d0301c6843e_0.jpeg"> ISUP 1: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0cee71ab57422e04f76e09ef2186fcd5_1.jpeg"> ISUP 2: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg"> ISUP 3: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c5c2d16c0f2e399b7be641e7e7f66d9_3.jpeg"> ISUP 4: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c88d7c7033e2048b1068e208b105270_4.jpeg"> ISUP 5: <img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00c15b23b30a5ba061358d9641118904_5.jpeg">
RUCAIBox/mvp-summarization
8b0bfb62f0c9b8a2b8ff05d3812903536afa3d4e
2022-06-27T02:28:20.000Z
[ "pytorch", "mvp", "en", "arxiv:2206.12131", "transformers", "text-generation", "text2text-generation", "summarization", "license:apache-2.0" ]
text2text-generation
false
RUCAIBox
null
RUCAIBox/mvp-summarization
58
null
transformers
5,743
--- license: apache-2.0 language: - en tags: - text-generation - text2text-generation - summarization pipeline_tag: text2text-generation widget: - text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons." example_title: "Example1" - text: "Summarize: Jorge Alfaro drove in two runs, Aaron Nola pitched seven innings of two-hit ball and the Philadelphia Phillies beat the Los Angeles Dodgers 2-1 Thursday, spoiling Clayton Kershaw's first start in almost a month. Hitting out of the No. 8 spot in the ..." example_title: "Example2" --- # MVP-summarization The MVP-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). ## Model Description MVP-summarization is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled summarization datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts. MVP-summarization is specially designed for summarization tasks, such as new summarization (CNN/DailyMail, XSum) and dialog summarization (SAMSum). ## Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-summarization") >>> inputs = tokenizer( ... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ["Don't do it if these are your reasons"] ``` ## Related Models **MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp). **Prompt-based models**: - MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task). - MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization). - MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog). - MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text). - MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story). - MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering). - MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation). - MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog). **Multi-task models**: - MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization). - MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog). - MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text). - MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story). - MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering). - MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation). - MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog). ## Citation ```bibtex @article{tang2022mvp, title={MVP: Multi-task Supervised Pre-training for Natural Language Generation}, author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2206.12131}, year={2022}, url={https://arxiv.org/abs/2206.12131}, } ```
AryaSuprana/BRATA_RoBERTaBali
7b7518cbe564d329a9869059874d8b160cb25e20
2022-06-11T05:01:40.000Z
[ "pytorch", "tensorboard", "roberta", "fill-mask", "ban", "dataset:WikiBali", "dataset:Suara Saking Bali", "transformers", "autotrain_compatible" ]
fill-mask
false
AryaSuprana
null
AryaSuprana/BRATA_RoBERTaBali
58
null
transformers
5,744
--- language: "ban" datasets: - WikiBali - Suara Saking Bali widget: - text: "Kalsium silih <mask> datu kimia antuk simbol Ca miwah wilangan atom 20." example_title: "Conto 1" - text: "Tabuan inggih <mask> silih tunggil soroh beburon sane madue kampid." example_title: "Conto 2" --- BRATA (Basa Bali Used for Pretraining RoBERTa) is a pretrained language model trained using Basa Bali or Balinese Language with RoBERTa-base-uncased configuration. The datasets used for this pretraining were collected by extracting WikiBali or Wikipedia Basa Bali and some sources from Suara Saking Bali website. The pretrained language model trained using Google Colab Pro with Tesla P100-PCIE-16GB GPU. Pretraining process used 200 epoch and 2 batch size. The smallest training loss can be seen in Training metrics or Metrics tab.
Anjoe/gbert-large
0d03ce7b19d93c48c43bf2cd9640af58bcfaa536
2022-06-25T17:08:58.000Z
[ "pytorch", "tf", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
fill-mask
false
Anjoe
null
Anjoe/gbert-large
58
null
transformers
5,745
--- tags: - generated_from_trainer model-index: - name: gbert-large results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gbert-large This model is a fine-tuned version of deepset/gbert-large It was fine-tuned on poetry from Projekt Gutenberg in order to do masked language modeling tasks in poetry generation (synonym creation for rythm and to find rhyming pairs) - Loss: 2.1519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7515 | 1.0 | 2481 | 2.5493 | | 2.5626 | 2.0 | 4962 | 2.4392 | | 2.4839 | 3.0 | 7443 | 2.3692 | | 2.4082 | 4.0 | 9924 | 2.3425 | | 2.3109 | 5.0 | 12405 | 2.2934 | | 2.2551 | 6.0 | 14886 | 2.2582 | | 2.2154 | 7.0 | 17367 | 2.2062 | | 2.2003 | 8.0 | 19848 | 2.1962 | | 2.1616 | 9.0 | 22329 | 2.1991 | | 2.1462 | 10.0 | 24810 | 2.1519 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
ArnavL/roberta-base-agnews-0
9e3cf04110af9982ee2b4c18a7d310fd35d6255e
2022-07-12T00:34:24.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
ArnavL
null
ArnavL/roberta-base-agnews-0
58
null
transformers
5,746
Entry not found
kaj/evoker
640000717de9e5645bd524c391b2271564abd2db
2022-07-26T02:34:16.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "license:mit" ]
text-generation
false
kaj
null
kaj/evoker
58
1
transformers
5,747
--- license: mit ---
Salesforce/qaconv-unifiedqa-t5-base
ff060b2e623e10ebabcfec840809eb4ddd7f92d2
2021-06-23T10:13:43.000Z
[ "pytorch", "tf", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Salesforce
null
Salesforce/qaconv-unifiedqa-t5-base
57
null
transformers
5,748
Entry not found
TransQuest/microtransquest-en_lv-pharmaceutical-nmt
983d0506c36d60d468a60a16b4e4a0a9f799a48b
2021-06-04T08:21:54.000Z
[ "pytorch", "xlm-roberta", "token-classification", "en-lv", "transformers", "Quality Estimation", "microtransquest", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
TransQuest
null
TransQuest/microtransquest-en_lv-pharmaceutical-nmt
57
null
transformers
5,749
--- language: en-lv tags: - Quality Estimation - microtransquest license: apache-2.0 --- # TransQuest: Translation Quality Estimation with Cross-lingual Transformers The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest). ## Features - Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment. - Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps. - Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. - Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest) ## Installation ### From pip ```bash pip install transquest ``` ### From Source ```bash git clone https://github.com/TharinduDR/TransQuest.git cd TransQuest pip install -r requirements.txt ``` ## Using Pre-trained Models ```python from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel import torch model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_lv-pharmaceutical-nmt", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available()) source_tags, target_tags = model.predict([["if not , you may not be protected against the diseases . ", "ja tā nav , Jūs varat nepasargāt no slimībām . "]]) ``` ## Documentation For more details follow the documentation. 1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip. 2. **Architectures** - Checkout the architectures implemented in TransQuest 1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation. 2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks. 1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/) 2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/) 4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level 1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/) 2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/) 5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest ## Citations If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/). ```bash @InProceedings{ranasinghe2021, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics}, year = {2021} } ``` If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020. ```bash @InProceedings{transquest:2020a, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, year = {2020} } ``` ```bash @InProceedings{transquest:2020b, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest at WMT2020: Sentence-Level Direct Assessment}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, year = {2020} } ```
UBC-NLP/AraT5-msa-small
dcf01ca943e90f2777d38c36d27d090203096a34
2022-05-26T18:27:22.000Z
[ "pytorch", "tf", "t5", "ar", "transformers", "Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation" ]
null
false
UBC-NLP
null
UBC-NLP/AraT5-msa-small
57
2
transformers
5,750
--- language: - ar tags: - Arabic T5 - MSA - Twitter - Arabic Dialect - Arabic Machine Translation - Arabic Text Summarization - Arabic News Title and Question Generation - Arabic Paraphrasing and Transliteration - Arabic Code-Switched Translation --- # AraT5-msa-small # AraT5: Text-to-Text Transformers for Arabic Language Generation <img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/> This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models; --- # How to use AraT5 models Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset ``` bash !python run_trainier_seq2seq_huggingface.py \ --learning_rate 5e-5 \ --max_target_length 128 --max_source_length 128 \ --per_device_train_batch_size 8 --per_device_eval_batch_size 8 \ --model_name_or_path "UBC-NLP/AraT5-base" \ --output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \ --num_train_epochs 3 \ --train_file "/content/ARGEn_title_genration_sample_train.tsv" \ --validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \ --task "title_generation" --text_column "document" --summary_column "title" \ --load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\ --do_train --do_eval ``` For more details about the fine-tuning example, please read this notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb) In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)). For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5). # AraT5 Models Checkpoints AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).``` | **Model** | **Link** | |---------|:------------------:| | **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) | | **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) | | **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) | | **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) | | **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) | # BibTex If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): ```bibtex @inproceedings{nagoudi-etal-2022-arat5, title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation", author = "Nagoudi, El Moatez Billah and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.47", pages = "628--647", abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.", } ``` ## Acknowledgments We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
abhishek/autonlp-japanese-sentiment-59362
984f7c46692dcda527a9c985a47b115318bcfd13
2021-05-18T22:55:03.000Z
[ "pytorch", "jax", "bert", "text-classification", "ja", "dataset:abhishek/autonlp-data-japanese-sentiment", "transformers", "autonlp" ]
text-classification
false
abhishek
null
abhishek/autonlp-japanese-sentiment-59362
57
1
transformers
5,751
--- tags: autonlp language: ja widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-japanese-sentiment --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 59362 ## Validation Metrics - Loss: 0.13092292845249176 - Accuracy: 0.9527127414314258 - Precision: 0.9634070704982427 - Recall: 0.9842171959602166 - AUC: 0.9667289746092403 - F1: 0.9737009564152002 ## 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/abhishek/autonlp-japanese-sentiment-59362 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
akdeniz27/bert-base-turkish-cased-ner
53ddeb6e865ad7248f002ea6461c5b5fb0ecfa46
2021-10-14T19:50:00.000Z
[ "pytorch", "bert", "token-classification", "tr", "transformers", "autotrain_compatible" ]
token-classification
false
akdeniz27
null
akdeniz27/bert-base-turkish-cased-ner
57
1
transformers
5,752
--- language: tr widget: - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı." --- # Turkish Named Entity Recognition (NER) Model This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "dbmdz/bert-base-turkish-cased" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 3 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") ner("<your text here>") ``` Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. # Reference test results: * accuracy: 0.9933935699477056 * f1: 0.9592969472710453 * precision: 0.9543530277931161 * recall: 0.9642923563325274 Evaluation results with the test sets proposed in ["Küçük, D., Küçük, D., Arıcı, N. 2016. Türkçe Varlık İsmi Tanıma için bir Veri Kümesi ("A Named Entity Recognition Dataset for Turkish"). IEEE Sinyal İşleme, İletişim ve Uygulamaları Kurultayı. Zonguldak, Türkiye."](https://ieeexplore.ieee.org/document/7495744) paper. * Test Set Acc. Prec. Rec. F1-Score * 20010000 0.9946 0.9871 0.9463 0.9662 * 20020000 0.9928 0.9134 0.9206 0.9170 * 20030000 0.9942 0.9814 0.9186 0.9489 * 20040000 0.9943 0.9660 0.9522 0.9590 * 20050000 0.9971 0.9539 0.9932 0.9732 * 20060000 0.9993 0.9942 0.9942 0.9942 * 20070000 0.9970 0.9806 0.9439 0.9619 * 20080000 0.9988 0.9821 0.9649 0.9735 * 20090000 0.9977 0.9891 0.9479 0.9681 * 20100000 0.9961 0.9684 0.9293 0.9485 * Overall 0.9961 0.9720 0.9516 0.9617
averyanalex/panorama-rugpt3large
e1adee6678c3a362036fa572cd9bad40e4c3ba66
2022-01-13T12:54:41.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
averyanalex
null
averyanalex/panorama-rugpt3large
57
null
transformers
5,753
Entry not found
cahya/wav2vec2-large-xlsr-turkish-artificial-cv
c082543f4ffe0da89ff44b02a0ec7c7dc65afdff
2021-07-06T00:02:23.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "tr", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
cahya
null
cahya/wav2vec2-large-xlsr-turkish-artificial-cv
57
null
transformers
5,754
--- language: tr datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Turkish by Cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 14.61 --- # Wav2Vec2-Large-XLSR-Turkish This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned [cahya/wav2vec2-large-xlsr-turkish-artificial](https://huggingface.co/cahya/wav2vec2-large-xlsr-turkish-artificial) model on [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 14.61 % ## Training The Common Voice `train`, `validation`, other and invalidated The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
cosmoquester/bart-ko-base
ed861421e349d4f8bed6667fb2dd8848eb24dce3
2021-08-28T05:12:02.000Z
[ "pytorch", "tf", "bart", "text2text-generation", "ko", "transformers", "autotrain_compatible" ]
text2text-generation
false
cosmoquester
null
cosmoquester/bart-ko-base
57
1
transformers
5,755
--- language: ko --- # Pretrained BART in Korean This is pretrained BART model with multiple Korean Datasets. I used multiple datasets for generalizing the model for both colloquial and written texts. The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain). When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example. ``` [BOS] 안녕하세요? 반가워요~~ [EOS] ``` You can also test mask filling performance using `[MASK]` token like this. ``` [BOS] [MASK] 먹었어? [EOS] ``` ## Benchmark <style> table { border-collapse: collapse; border-style: hidden; width: 100%; } td, th { border: 1px solid #4d5562; padding: 8px; } </style> <table> <tr> <th>Dataset</th> <td>KLUE NLI dev</th> <td>NSMC test</td> <td>QuestionPair test</td> <td colspan="2">KLUE TC dev</td> <td colspan="3">KLUE STS dev</td> <td colspan="3">KorSTS dev</td> <td colspan="2">HateSpeech dev</td> </tr> <tr> <th>Metric</th> <!-- KLUE NLI --> <td>Acc</th> <!-- NSMC --> <td>Acc</td> <!-- QuestionPair --> <td>Acc</td> <!-- KLUE TC --> <td>Acc</td> <td>F1</td> <!-- KLUE STS --> <td>F1</td> <td>Pearson</td> <td>Spearman</td> <!-- KorSTS --> <td>F1</td> <td>Pearson</td> <td>Spearman</td> <!-- HateSpeech --> <td>Bias Acc</td> <td>Hate Acc</td> </tr> <tr> <th>Score</th> <!-- KLUE NLI --> <td>0.7390</th> <!-- NSMC --> <td>0.8877</td> <!-- QuestionPair --> <td>0.9208</td> <!-- KLUE TC --> <td>0.8667</td> <td>0.8637</td> <!-- KLUE STS --> <td>0.7654</td> <td>0.8090</td> <td>0.8040</td> <!-- KorSTS --> <td>0.8067</td> <td>0.7909</td> <td>0.7784</td> <!-- HateSpeech --> <td>0.8280</td> <td>0.5669</td> </tr> </table> - The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab. ## Used Datasets ### [모두의 말뭉치](https://corpus.korean.go.kr/) - 일상 대화 말뭉치 2020 - 구어 말뭉치 - 문어 말뭉치 - 신문 말뭉치 ### AIhub - [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717) - [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714) - [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978) - [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105) - [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718) ### [세종 말뭉치](https://ithub.korean.go.kr/)
gagan3012/keytotext-small
bf3e0c8f9aab3d0cdf83db549fb7003920017ee6
2021-03-11T23:33:47.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
gagan3012
null
gagan3012/keytotext-small
57
null
transformers
5,756
# 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: [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/gagan3012/keytotext/app.py) https://share.streamlit.io/gagan3012/keytotext/app.py ![image](https://user-images.githubusercontent.com/49101362/110660053-3b20fe80-81d4-11eb-9275-ba402134e8d9.png) ### Example: ['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
google/bert_uncased_L-8_H-128_A-2
015be1772f53aaa32683e3bd44ab075708f25812
2021-05-19T17:35:05.000Z
[ "pytorch", "jax", "bert", "arxiv:1908.08962", "transformers", "license:apache-2.0" ]
null
false
google
null
google/bert_uncased_L-8_H-128_A-2
57
null
transformers
5,757
--- thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- BERT Miniatures === This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |H=128|H=256|H=512|H=768| |---|:---:|:---:|:---:|:---:| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. Here are the corresponding GLUE scores on the test set: |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: - batch sizes: 8, 16, 32, 64, 128 - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 If you use these models, please cite the following paper: ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ``` [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12
jcpwfloi/gpt2-story-generation
b2acb595321a3c3cd0f05b7eb035132d8acda70b
2021-05-23T05:48:11.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
jcpwfloi
null
jcpwfloi/gpt2-story-generation
57
null
transformers
5,758
Entry not found
monologg/koelectra-base-finetuned-sentiment
5bb90123f35cc220ed2d2ae1c157af265748c7bb
2020-05-14T02:30:04.000Z
[ "pytorch", "electra", "text-classification", "transformers" ]
text-classification
false
monologg
null
monologg/koelectra-base-finetuned-sentiment
57
null
transformers
5,759
Entry not found
msintaha/bert-base-uncased-finetuned-copa-data-new
c7c9123706f1e0f45617181ccf4bd25c227cff66
2022-02-15T08:41:46.000Z
[ "pytorch", "tensorboard", "bert", "multiple-choice", "dataset:super_glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
multiple-choice
false
msintaha
null
msintaha/bert-base-uncased-finetuned-copa-data-new
57
null
transformers
5,760
--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-copa-data-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-copa-data-new This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5995 - Accuracy: 0.7000 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 25 | 0.6564 | 0.6600 | | No log | 2.0 | 50 | 0.5995 | 0.7000 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
pucpr/clinicalnerpt-finding
b61d343277019091c349926424c0996488ffb414
2021-10-13T09:32:39.000Z
[ "pytorch", "bert", "token-classification", "pt", "dataset:SemClinBr", "transformers", "autotrain_compatible" ]
token-classification
false
pucpr
null
pucpr/clinicalnerpt-finding
57
4
transformers
5,761
--- language: "pt" widget: - text: "RECEBE ALTA EM BOM ESTADO GERAL, COM PLANO DE ACOMPANHAR NO AMBULATÓRIO." - text: "PACIENTE APRESENTOU BOA EVOLUÇÃO CLÍNICA APÓS OTIMIZAÇÃO DO TTO DA ICC." datasets: - SemClinBr thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # Portuguese Clinical NER - Finding The Finding NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus [SemClinBr](https://github.com/HAILab-PUCPR/SemClinBr), with 10 epochs and IOB2 format, from BioBERTpt(all) model. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).
sail/poolformer_m48
752b4a4de30d3fb40c59db413fa6f9ce51be2cc0
2022-04-08T07:48:25.000Z
[ "pytorch", "poolformer", "image-classification", "dataset:imagenet", "arxiv:2111.11418", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
sail
null
sail/poolformer_m48
57
null
transformers
5,762
--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # PoolFormer (M48 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_m48') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_m48') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | # params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://huggingface.co/sail/poolformer_s12 | | PoolFormer-S24 | 80.3 | 21M | https://huggingface.co/sail/poolformer_s24 | | PoolFormer-S36 | 81.4 | 31M | https://huggingface.co/sail/poolformer_s36 | | PoolFormer-M36 | 82.1 | 56M | https://huggingface.co/sail/poolformer_m36 | | **PoolFormer-M48** | **82.5** | **73M** | **https://huggingface.co/sail/poolformer_m48** | ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```
seduerr/t5-pawraphrase
afb526cf961ea8113dbb7356df14a599eb7c3876
2021-06-23T14:19:12.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
seduerr
null
seduerr/t5-pawraphrase
57
null
transformers
5,763
# Invoking more Creativity with Pawraphrases based on T5 ## This micro-service allows to find paraphrases for a given text based on T5. ![Imgur](https://i.imgur.com/v6DFBE0.png) We explain how we finetune the architecture T5 with the dataset PAWS (both from Google) to get the capability of creating paraphrases (or pawphrases since we are using the PAWS dataset :smile:). With this, we can create paraphrases for any given textual input. Find the code for the service in this [Github Repository](https://github.com/seduerr91/pawraphrase_public). In order to create your own __'pawrasphrase tool'__, follow these steps: ### Step 1: Find a Useful Architecture and Datasets Since Google's [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) has been trained on multiple tasks (e.g., text summarization, question-answering) and it is solely based on Text-to-Text tasks it is pretty useful for extending its task-base through finetuning it with paraphrases. Luckily, the [PAWS](https://github.com/google-research-datasets/paws) dataset consists of approximately 50.000 labeled paraphrases that we can use to fine-tune T5. ### Step 2: Prepare the PAWS Dataset for the T5 Architecture Once identified, it is crucial to prepare the PAWS dataset to feed it into the T5 architecture for finetuning. Since PAWS is coming both with paraphrases and non-paraphases, it needs to be filtered for paraphrases only. Next, after packing it into a Pandas DataFrame, the necessary table headers had to be created. Next, you split the resulting training samples into test, train, and validation set. ![Imgur](https://i.imgur.com/MTM6apI.png) ### Step 3: Fine-tune T5-base with PAWS Next, following these [training instructions](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555), in which they used the Quora dataset, we use the PAWS dataset and feed into T5. Central is the following code to ensure that T5 understands that it has to _paraphrase_. The adapted version can be found [here](https://github.com/seduerr91/pawraphrase_public/blob/master/t5_pawraphrase_training.ipynb). ![Imgur](https://i.imgur.com/uAd0bVo.png) Additionally, it is helpful to force the old versions of _torch==1.4.0, transformers==2.9.0_ and *pytorch_lightning==0.7.5*, since the newer versions break (trust me, I am speaking from experience). However, when doing such training, it is straightforward to start with the smallest architecture (here, _T5-small_) and a small version of your dataset (e.g., 100 paraphrase examples) to quickly identify where the training may fail or stop. ### Step 4: Start Inference by yourself. Next, you can use the fine-tuned T5 Architecture to create paraphrases from every input. As seen in the introductory image. The corresponding code can be found [here](https://github.com/seduerr91/pawraphrase_public/blob/master/t5_pawraphrase_inference.ipynb). ### Step 5: Using the fine-tuning through a GUI Finally, to make the service useful we can provide it as an API as done with the infilling model [here](https://seduerr91.github.io/blog/ilm-fastapi) or with this [frontend](https://github.com/renatoviolin/T5-paraphrase-generation) which was prepared by Renato. Kudos! Thank you for reading this article. I'd be curious about your opinion. #### Who am I? I am Sebastian an NLP Deep Learning Research Scientist (M.Sc. in IT and Business). In my former life, I was a manager at Austria's biggest bank. In the future, I want to work remotely flexibly & in the field of NLP. Drop me a message on [LinkedIn](https://www.linkedin.com/in/sebastianduerr/) if you want to get in touch!
textattack/xlnet-large-cased-SST-2
d2e7fe2c3bc11df01de21b76788559740b688921
2020-06-09T16:59:05.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
false
textattack
null
textattack/xlnet-large-cased-SST-2
57
0
transformers
5,764
Entry not found
tiedeman/opus-mt-en-he
4fb656f23568471c76209d2582cd27a7645f8c15
2021-03-04T17:50:20.000Z
[ "pytorch", "rust", "marian", "text2text-generation", "en", "he", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
tiedeman
null
tiedeman/opus-mt-en-he
57
null
transformers
5,765
--- 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
microsoft/tapex-base-finetuned-tabfact
64c585e9e0784c175d8eddb0ee17974c260f1766
2022-07-14T10:09:42.000Z
[ "pytorch", "bart", "text-classification", "en", "dataset:tab_fact", "arxiv:2107.07653", "transformers", "tapex", "license:mit" ]
text-classification
false
microsoft
null
microsoft/tapex-base-finetuned-tabfact
57
null
transformers
5,766
--- language: en tags: - tapex datasets: - tab_fact license: mit --- # TAPEX (base-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). ## Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. This model is the `tapex-base` model fine-tuned on the [Tabfact](https://huggingface.co/datasets/tab_fact) dataset. ## Intended Uses You can use the model for table fact verficiation. ### How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForSequenceClassification import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-tabfact") model = BartForSequenceClassification.from_pretrained("microsoft/tapex-base-finetuned-tabfact") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) # tapex accepts uncased input since it is pre-trained on the uncased corpus query = "beijing hosts the olympic games in 2012" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model(**encoding) output_id = int(outputs.logits[0].argmax(dim=0)) print(model.config.id2label[output_id]) # Refused ``` ### How to Eval Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex). ### BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ```
ali2066/bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25
37d6511313dffd6e304cab976cb111b879f29857
2022-03-01T13:24:47.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25
57
null
transformers
5,767
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased_token_itr0_0.0001_all_01_03_2022-14_21_25 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2698 - Precision: 0.3321 - Recall: 0.5265 - F1: 0.4073 - Accuracy: 0.8942 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.3314 | 0.1627 | 0.3746 | 0.2269 | 0.8419 | | No log | 2.0 | 60 | 0.2957 | 0.2887 | 0.4841 | 0.3617 | 0.8592 | | No log | 3.0 | 90 | 0.2905 | 0.2429 | 0.5141 | 0.3299 | 0.8651 | | No log | 4.0 | 120 | 0.2759 | 0.3137 | 0.5565 | 0.4013 | 0.8787 | | No log | 5.0 | 150 | 0.2977 | 0.3116 | 0.5565 | 0.3995 | 0.8796 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
jkhan447/sentiment-model-sample-group-emotion
50852be49c7255937fb8baada134d7a65e6c6aad
2022-03-23T08:19:54.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
jkhan447
null
jkhan447/sentiment-model-sample-group-emotion
57
null
transformers
5,768
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sentiment-model-sample-group-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-model-sample-group-emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4604 - Accuracy: 0.7004 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
afbudiman/indobert-classification
9847311f7585cf858a1ba9cb535b3cd88e421a02
2022-05-06T12:54:14.000Z
[ "pytorch", "bert", "text-classification", "dataset:indonlu", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
afbudiman
null
afbudiman/indobert-classification
57
null
transformers
5,769
--- license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9396825396825397 - name: F1 type: f1 value: 0.9393057427148881 --- <!-- 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. --> # indobert-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.3707 - Accuracy: 0.9397 - F1: 0.9393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2458 | 1.0 | 688 | 0.2229 | 0.9325 | 0.9323 | | 0.1258 | 2.0 | 1376 | 0.2332 | 0.9373 | 0.9369 | | 0.059 | 3.0 | 2064 | 0.3389 | 0.9365 | 0.9365 | | 0.0268 | 4.0 | 2752 | 0.3412 | 0.9421 | 0.9417 | | 0.0097 | 5.0 | 3440 | 0.3707 | 0.9397 | 0.9393 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
Finnish-NLP/byt5-base-finnish
6b5217510aa375669642c64533388b571dd87e6f
2022-07-12T14:22:08.000Z
[ "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "fi", "dataset:Finnish-NLP/mc4_fi_cleaned", "dataset:wikipedia", "arxiv:2105.13626", "arxiv:2002.05202", "transformers", "finnish", "byt5", "t5x", "seq2seq", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
Finnish-NLP
null
Finnish-NLP/byt5-base-finnish
57
null
transformers
5,770
--- language: - fi license: apache-2.0 tags: - finnish - t5 - byt5 - t5x - seq2seq datasets: - Finnish-NLP/mc4_fi_cleaned - wikipedia inference: false --- # ByT5-base for Finnish Pretrained ByT5 model on Finnish language using a span-based masked language modeling (MLM) objective. ByT5 was introduced in [this paper](https://arxiv.org/abs/2105.13626) and first released at [this page](https://github.com/google-research/byt5). **Note:** The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice. As an example of a fine-tuned Finnish T5 model, you can check [Finnish-NLP/t5-small-nl24-casing-punctuation-correction](https://huggingface.co/Finnish-NLP/t5-small-nl24-casing-punctuation-correction) which has been fine-tuned to correct missing casing and punctuation for Finnish text. ## Model description ByT5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. ByT5 is a tokenizer-free extension of the T5 model. Instead of using a subword vocabulary like most other pretrained language models (BERT, XLM-R, T5, GPT-3), ByT5 model operates directly on UTF-8 bytes, removing the need for any text preprocessing. ByT5 can outperform T5 models on tasks that involve noisy text or are sensitive to spelling and pronunciation. Finnish ByT5 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw 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 outputs from those texts. More precisely, it was pretrained with the span-based masked language modeling (MLM) objective with an average span-mask of 20 UTF-8 characters. Spans of the input sequence are masked by so-called sentinel tokens (a.k.a unique mask tokens) and the output sequence is formed as a concatenation of the same sentinel tokens and the real masked tokens. This way, the model learns an inner representation of the Finnish language. This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on span-based masked language modeling (MLM) objective only without mixing in the downstream tasks - No parameter sharing between embedding and classifier layer This model uses the [google/byt5-base](https://huggingface.co/google/byt5-base) architecture which means the encoder has 18 transformer layers and the decoder has 6 transformer layers. The [ByT5 paper](https://arxiv.org/abs/2105.13626) found out that "heavier" encoder is beneficial in vocabulary-free models as ByT5. In total, this model has 582 million parameters. ## Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. **Note:** You most likely need to fine-tune these T5 models without mixed precision so fine-tune them with full fp32 precision. You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example. ### How to use **Note:** ByT5 works on raw UTF-8 bytes and can be used without a tokenizer. For batched inference & training it is however recommended using a tokenizer class for padding. Here is how to use this model in PyTorch: ```python from transformers import T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/byt5-base-finnish") ``` and in TensorFlow: ```python from transformers import TFT5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/byt5-base-finnish", from_pt=True) ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data This Finnish T5 model was pretrained on the combination of six datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo). - [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset - [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501) - [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401) - [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001) - [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803) Raw datasets were automatically cleaned to filter out bad quality and non-Finnish examples. Also, a [perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) score was calculated for all texts with a KenLM model which was trained with very clean Finnish texts only. This perplexity score can then be used to determine how "clean" Finnish language the text contains. Lastly, all datasets were concatenated and the top 90% perplexity score was used as a filtering threshold to filter out the worst quality 10% of texts. Together these cleaned datasets were around 76GB of text. ## Training procedure ### Preprocessing The inputs and the outputs are sequences of 512 consecutive bytes. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish. ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 850K steps with a batch size of 256 (in total 111B bytes). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and then an inverse square root decay (exponential decay) of the learning rate after. Training code was from the Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) and also some t5x task definitions were adapted from [Per's t5x work](https://huggingface.co/pere). ## Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length of 128 bytes. When fine-tuned on those datasets, this model (the fourth row of the table) achieves the following accuracy results compared to our other T5 models and their parameter counts: | | Model parameters | Yle News accuracy | Eduskunta accuracy | |-------------------------------------------------------|------------------|---------------------|----------------------| |Finnish-NLP/t5-tiny-nl6-finnish | 31 million |92.80 |69.07 | |Finnish-NLP/t5-mini-nl8-finnish | 72 million |93.89 |71.43 | |Finnish-NLP/t5-small-nl24-finnish | 260 million |**94.68** |74.90 | |Finnish-NLP/byt5-base-finnish | 582 million |92.33 |73.13 | |Finnish-NLP/t5-base-nl36-finnish | 814 million |94.40 |**75.97** | |Finnish-NLP/t5-large-nl36-finnish | 1425 million |TBA |TBA | Fine-tuning Google's multilingual mT5 models on the same datasets we can clearly see that our monolingual Finnish T5 models achieve much better results on Finnish text classification: | | Model parameters | Yle News accuracy | Eduskunta accuracy | |-------------------------------------------------------|------------------|---------------------|----------------------| |google/mt5-small | 301 million |91.51 |64.10 | |google/mt5-base | 583 million |92.71 |68.40 | ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
IsaacRodgz/Tamil-Hate-Speech
7a5db650dc1a62398891d5bf50a10451ca2cb8ec
2022-06-02T22:05:58.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
IsaacRodgz
null
IsaacRodgz/Tamil-Hate-Speech
57
null
transformers
5,771
Entry not found
ccdv/lsg-bart-base-16384-mediasum
f7c6158dd1c5402e5afecc3c70d0958fc9fac2f7
2022-07-25T05:30:05.000Z
[ "pytorch", "bart", "text2text-generation", "en", "dataset:ccdv/mediasum", "transformers", "summarization", "model-index", "autotrain_compatible" ]
summarization
false
ccdv
null
ccdv/lsg-bart-base-16384-mediasum
57
null
transformers
5,772
--- language: - en tags: - summarization datasets: - ccdv/mediasum metrics: - rouge model-index: - name: ccdv/lsg-bart-base-16384-mediasum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> **This model relies on a custom modeling file, you need to add trust_remote_code=True**\ **See [\#13467](https://github.com/huggingface/transformers/pull/13467)** ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384-mediasum", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384-mediasum", trust_remote_code=True) text = "Replace by what you want." pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0) generated_text = pipe( text, truncation=True, max_length=64, no_repeat_ngram_size=7, num_beams=2, early_stopping=True ) ``` # ccdv/lsg-bart-base-16384-mediasum This model is a fine-tuned version of [ccdv/lsg-bart-base-4096-mediasum](https://huggingface.co/ccdv/lsg-bart-base-4096-mediasum) on the [ccdv/mediasum roberta_prepended mediasum](https://huggingface.co/datasets/ccdv/mediasum) dataset. \ The model is converted to handle 16384 long sequences and fine-tuned accordingly during 1 epoch. \ It achieves the following results on the test set: | Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | | 16384 | 64 | Full | 256 | 0 | 768 | 35.31 | 18.35 | 31.81 | 32.47 | | 16384 | 1 | Full | 256 | 0 | 768 | 35.21 | 18.20 | 31.73 | 32.37 | | 16384 | 64 | Global only | 256 | 0 | 768 | 35.22 | 18.08 | 31.54 | 32.21 | | 16384 | 1 | None | 256 | 0 | 768 | 35.17 | 18.13 | 31.54 | 32.20 | Reference model: | Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | | 4096 | 1 | - | 256 | 0 | 768 | 35.16 | 18.13 | 31.54 | 32.20 ## Model description The model relies on Local-Sparse-Global attention to handle long sequences: ![attn](attn.png) The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \ The model is warm started from [ccdv/lsg-bart-base-4096-mediasum](https://huggingface.co/ccdv/lsg-bart-base-4096-mediasum), converted to handle long sequences (encoder only) and fine tuned. ## 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: 8e-05 - train_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_ratio: 0.1 - num_epochs: 1.0 ### Generate hyperparameters The following hyperparameters were used during generation: - dataset_name: ccdv/mediasum - dataset_config_name: roberta_prepended - eval_batch_size: 8 - eval_samples: 10000 - early_stopping: True - ignore_pad_token_for_loss: True - length_penalty: 2.0 - max_length: 128 - min_length: 3 - num_beams: 5 - no_repeat_ngram_size: None - seed: 123 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.1+cu102 - Datasets 2.1.0 - Tokenizers 0.11.6
ClassCat/roberta-base-spanish
99bf4a2b27347fc92a75504fa89ce98c711f1245
2022-07-14T09:38:05.000Z
[ "pytorch", "roberta", "fill-mask", "es", "dataset:wikipedia", "dataset:cc100", "transformers", "license:cc-by-sa-4.0", "autotrain_compatible" ]
fill-mask
false
ClassCat
null
ClassCat/roberta-base-spanish
57
1
transformers
5,773
--- language: es license: cc-by-sa-4.0 datasets: - wikipedia - cc100 widget: - text: "Yo vivo en <mask>." - text: "Quiero <mask> contigo ?" - text: "Es clima es <mask>." - text: "Me llamo <mask>." - text: "Las negociaciones están <mask>." --- ## RoBERTa Spanish base model (Uncased) ### Prerequisites transformers==4.19.2 ### Model architecture This model uses RoBERTa base setttings except vocabulary size. ### Tokenizer Using BPE tokenizer with vocabulary size 50,000. ### Training Data * [wiki40b/es](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki40bes) (Spanish Wikipedia) * Subset of [CC-100/es](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data ### Usage ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='ClassCat/roberta-base-spanish') unmasker("Yo soy <mask>.") ```
Aktsvigun/bart-base_aeslc_705525
a34cc8dc35ea4ed1d4ccf8d8b5419a714180e1d7
2022-07-07T15:46:52.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Aktsvigun
null
Aktsvigun/bart-base_aeslc_705525
57
null
transformers
5,774
Entry not found
huggingtweets/frnsw-nswrfs-nswses
e98941d314f6a2f12f178225d9c318960231e426
2022-07-06T14:32:52.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/frnsw-nswrfs-nswses
57
null
transformers
5,775
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1150678663265832960/ujqrCyuu_400x400.png&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/895892720194957313/RVLTWlDI_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1500778204294180868/3B6rKocs_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">NSW RFS & NSW SES & Fire and Rescue NSW</div> <div style="text-align: center; font-size: 14px;">@frnsw-nswrfs-nswses</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from NSW RFS & NSW SES & Fire and Rescue NSW. | Data | NSW RFS | NSW SES | Fire and Rescue NSW | | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3248 | 3249 | | Retweets | 275 | 2093 | 875 | | Short tweets | 12 | 12 | 48 | | Tweets kept | 2963 | 1143 | 2326 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1cxt6027/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @frnsw-nswrfs-nswses's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tjbhow2z/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/frnsw-nswrfs-nswses') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
StanfordAIMI/covid-radbert
6519862ea4beae4199cac025f38274eba429ee34
2022-07-19T04:21:14.000Z
[ "pytorch", "bert", "en", "transformers", "text-classification", "uncased", "radiology", "biomedical", "covid-19", "covid19", "license:mit" ]
text-classification
false
StanfordAIMI
null
StanfordAIMI/covid-radbert
57
1
transformers
5,776
--- widget: - text: "procedure: single ap view of the chest comparison: none findings: no surgical hardware nor tubes. lungs, pleura: low lung volumes, bilateral airspace opacities. no pneumothorax or pleural effusion. cardiovascular and mediastinum: the cardiomediastinal silhouette seems stable. impression: 1. patchy bilateral airspace opacities, stable, but concerning for multifocal pneumonia. 2. absence of other suspicions, the rest of the lungs seems fine." - text: "procedure: single ap view of the chest comparison: none findings: No surgical hardware nor tubes. lungs, pleura: low lung volumes, bilateral airspace opacities. no pneumothorax or pleural effusion. cardiovascular and mediastinum: the cardiomediastinal silhouette seems stable. impression: 1. patchy bilateral airspace opacities, stable. 2. some areas are suggestive that pneumonia can not be excluded. 3. recommended to follow-up shortly and check if there are additional symptoms" tags: - text-classification - pytorch - transformers - uncased - radiology - biomedical - covid-19 - covid19 language: - en license: mit --- COVID-RadBERT was trained to detect the presence or absence of COVID-19 within radiology reports, along an "uncertain" diagnostic when further medical tests are required. Manuscript in-proceedings.
Kamrani/t5-large
aba0d1f6dc5c32b6e56a5c7c2733aa1aaf95d2f5
2022-07-27T13:13:19.000Z
[ "pytorch", "t5", "text2text-generation", "en", "fa", "arxiv:1805.12471", "arxiv:1708.00055", "arxiv:1704.05426", "arxiv:1606.05250", "arxiv:1808.09121", "arxiv:1810.12885", "arxiv:1905.10044", "arxiv:1910.09700", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Kamrani
null
Kamrani/t5-large
57
null
transformers
5,777
--- language: - en - fa tags: - translation license: apache-2.0 --- # Model Card for T5 Large ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67) # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-Large is the checkpoint with 770 million parameters. - **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - **Model type:** Language model - **Language(s) (NLP):** English, French, Romanian, German - **License:** Apache 2.0 - **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) - **Resources for more information:** - [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5) # Uses ## Direct Use and Downstream Use The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Recommendations More information needed. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr) 2. **Datasets used for Supervised text-to-text language modeling objective** - Sentence acceptability judgment - CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471) - Sentiment analysis - SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - Paraphrasing/sentence similarity - MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002) - STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055) - QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - Natural language inference - MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426) - QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250) - RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9) - CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf) - Sentence completion - COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning) - Word sense disambiguation - WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121) - Question answering - MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023) - ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885) - BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044) ## Training Procedure In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write: > In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details. ## Results For full results for T5-Large, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5Model tokenizer = T5Tokenizer.from_pretrained("t5-large") model = T5Model.from_pretrained("t5-large") input_ids = tokenizer( "Studies have been shown that owning a dog is good for you", return_tensors="pt" ).input_ids # Batch size 1 decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 # forward pass outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) last_hidden_states = outputs.last_hidden_state ``` See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more examples. </details>
Geotrend/bert-base-15lang-cased
787fc26696471d329e073e1a473adcf94de8c309
2022-06-28T08:50:42.000Z
[ "pytorch", "tf", "jax", "bert", "fill-mask", "multilingual", "en", "fr", "es", "de", "zh", "ar", "ru", "vi", "el", "bg", "th", "tr", "hi", "ur", "sw", "dataset:wikipedia", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
Geotrend
null
Geotrend/bert-base-15lang-cased
56
1
transformers
5,778
--- language: - multilingual - en - fr - es - de - zh - ar - ru - vi - el - bg - th - tr - hi - ur - sw datasets: wikipedia license: apache-2.0 widget: - text: "Google generated 46 billion [MASK] in revenue." - text: "Paris is the capital of [MASK]." - text: "Algiers is the largest city in [MASK]." - text: "Paris est la [MASK] de la France." - text: "Paris est la capitale de la [MASK]." - text: "L'élection américaine a eu [MASK] en novembre 2020." - text: "تقع سويسرا في [MASK] أوروبا" - text: "إسمي محمد وأسكن في [MASK]." --- # bert-base-15lang-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. The measurements below have been computed on a [Google Cloud n1-standard-1 machine (1 vCPU, 3.75 GB)](https://cloud.google.com/compute/docs/machine-types\#n1_machine_type): | Model | Num parameters | Size | Memory | Loading time | | ------------------------------- | -------------- | -------- | -------- | ------------ | | bert-base-multilingual-cased | 178 million | 714 MB | 1400 MB | 4.2 sec | | Geotrend/bert-base-15lang-cased | 141 million | 564 MB | 1098 MB | 3.1 sec | Handled languages: en, fr, es, de, zh, ar, ru, vi, el, bg, th, tr, hi, ur and sw. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-15lang-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-15lang-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Multilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
IDEA-CCNL/Randeng-MegatronT5-770M
d9f1391038fd66413915bb65036a99f2c5d52a24
2022-04-12T01:50:56.000Z
[ "pytorch", "t5", "text2text-generation", "zh", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
IDEA-CCNL
null
IDEA-CCNL/Randeng-MegatronT5-770M
56
5
transformers
5,779
--- language: - zh license: apache-2.0 inference: false --- # Randeng-MegatronT5-770M model (Chinese),one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). The 770 million parameter Randeng-770M large model, using 280G Chinese data, 16 A100 training for 14 days,which is a standard transformer structure. ## Usage There is no structure of Randeng-MegatronT5-770M in [Transformers](https://github.com/huggingface/transformers), you can run follow code to get structure of Randeng-MegatronT5-770M from [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ## Usage ```python from fengshen import T5ForConditionalGeneration from fengshen import T5Config from fengshen import T5Tokenizer tokenizer = T5Tokenizer.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') config = T5Config.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') model = T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') ``` ## Citation If you find the resource is useful, please cite the following website in your paper. ``` @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```
Raychanan/chinese-roberta-wwm-ext-FineTuned
980284db7a4b313adbfaf7504689a3668d22e829
2021-05-18T21:57:46.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
Raychanan
null
Raychanan/chinese-roberta-wwm-ext-FineTuned
56
null
transformers
5,780
Entry not found
adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717
5deecb5ec054e452417edb169c441b669e069908
2021-10-15T19:12:03.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:adrianmoses/autonlp-data-auto-nlp-lyrics-classification", "transformers", "autonlp", "co2_eq_emissions" ]
text-classification
false
adrianmoses
null
adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717
56
null
transformers
5,781
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - adrianmoses/autonlp-data-auto-nlp-lyrics-classification co2_eq_emissions: 88.89388195672073 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 19333717 - CO2 Emissions (in grams): 88.89388195672073 ## Validation Metrics - Loss: 1.0499154329299927 - Accuracy: 0.6207088513638894 - Macro F1: 0.46250803661544765 - Micro F1: 0.6207088513638894 - Weighted F1: 0.5850362079928957 - Macro Precision: 0.6451479987704787 - Micro Precision: 0.6207088513638894 - Weighted Precision: 0.6285080101186085 - Macro Recall: 0.4405680478429344 - Micro Recall: 0.6207088513638894 - Weighted Recall: 0.6207088513638894 ## 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/adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
flair/frame-english
09e0348db9809c2e50bf7135a2102817f6155fbf
2021-03-02T22:02:55.000Z
[ "pytorch", "en", "dataset:ontonotes", "flair", "token-classification", "sequence-tagger-model" ]
token-classification
false
flair
null
flair/frame-english
56
null
flair
5,782
--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - ontonotes widget: - text: "George returned to Berlin to return his hat." --- ## English Verb Disambiguation in Flair (default model) This is the standard verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **89,34** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/). 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/frame-english") # make example sentence sentence = Sentence("George returned to Berlin to return his hat.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following frame tags are found:') # iterate over entities and print for entity in sentence.get_spans('frame'): print(entity) ``` This yields the following output: ``` Span [2]: "returned" [− Labels: return.01 (0.9951)] Span [6]: "return" [− Labels: return.02 (0.6361)] ``` So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*". --- ### 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 ColumnCorpus from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself) corpus = ColumnCorpus( "resources/tasks/srl", column_format={1: "text", 11: "frame"} ) # 2. what tag do we want to predict? tag_type = 'frame' # 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 = [ BytePairEmbeddings("en"), FlairEmbeddings("news-forward"), FlairEmbeddings("news-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/frame-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2019flair, 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={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)}, pages={54--59}, year={2019} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
gchhablani/bert-base-cased-finetuned-stsb
56e68a16be5d3c7647447dc0512261fbe3dd9f99
2021-09-20T09:09:31.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "en", "dataset:glue", "arxiv:2105.03824", "transformers", "generated_from_trainer", "fnet-bert-base-comparison", "license:apache-2.0", "model-index" ]
text-classification
false
gchhablani
null
gchhablani/bert-base-cased-finetuned-stsb
56
null
transformers
5,783
--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - spearmanr model-index: - name: bert-base-cased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8897907271421561 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - Pearson: 0.8926 - Spearmanr: 0.8898 - Combined Score: 0.8912 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:| | 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 | | 0.3835 | 2.0 | 720 | 0.8901 | 0.4672 | 0.8915 | 0.8888 | | 0.2388 | 3.0 | 1080 | 0.8912 | 0.4861 | 0.8926 | 0.8898 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
google/tapas-large-finetuned-wikisql-supervised
77ca7d982ca59b32a97ee35d211db21086d41403
2021-11-29T13:05:23.000Z
[ "pytorch", "tf", "tapas", "table-question-answering", "en", "dataset:wikisql", "arxiv:2004.02349", "arxiv:2010.00571", "arxiv:1709.00103", "transformers", "license:apache-2.0" ]
table-question-answering
false
google
null
google/tapas-large-finetuned-wikisql-supervised
56
1
transformers
5,784
--- language: en tags: - tapas license: apache-2.0 datasets: - wikisql --- # TAPAS large model fine-tuned on WikiSQL (in a supervised fashion) his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_large_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 in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), and [WikiSQL](https://github.com/salesforce/WikiSQL). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is: - `no_reset`, which corresponds to `tapas_wikisql_sqa_inter_masklm_large` (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 and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQA and WikiSQL. ## Intended uses & limitations You can use this model for answering questions related to a table. 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] ``` The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts. ### Fine-tuning The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512. In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup ratio of 0.1424. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12). ### 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 @article{DBLP:journals/corr/abs-1709-00103, author = {Victor Zhong and Caiming Xiong and Richard Socher}, title = {Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, journal = {CoRR}, volume = {abs/1709.00103}, year = {2017}, url = {http://arxiv.org/abs/1709.00103}, archivePrefix = {arXiv}, eprint = {1709.00103}, timestamp = {Mon, 13 Aug 2018 16:48:41 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1709-00103.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
huggingtweets/abdi_smokes
053c1e273c5ac057b02362253c37bccaaae753e4
2021-05-21T17:24:13.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/abdi_smokes
56
1
transformers
5,785
--- language: en thumbnail: https://www.huggingtweets.com/abdi_smokes/1618179038747/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369684058037493763/sSdQ_pIn_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Mr. Dj💿 🤖 AI Bot </div> <div style="font-size: 15px">@abdi_smokes bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@abdi_smokes's tweets](https://twitter.com/abdi_smokes). | Data | Quantity | | --- | --- | | Tweets downloaded | 3185 | | Retweets | 172 | | Short tweets | 790 | | Tweets kept | 2223 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33jkx1b1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @abdi_smokes's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33g19os5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33g19os5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/abdi_smokes') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/furrymicky
e5ddb65505694397a4c073b1f08fa9c0eeb19c6a
2021-05-22T04:53:32.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/furrymicky
56
null
transformers
5,786
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1339300525007835137/YpAMPovA_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Micky The Weirdo from Taranto 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@furrymicky bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@furrymicky's tweets](https://twitter.com/furrymicky). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>459</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>91</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>354</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/109l35nl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @furrymicky's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/q3uw2fui) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/q3uw2fui/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/furrymicky'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
hugorosen/flaubert_base_uncased-xnli-sts
20c8c6c4bf023e1882ed8e1fb94b7a0a061db375
2021-11-30T15:36:33.000Z
[ "pytorch", "flaubert", "feature-extraction", "fr", "dataset:xnli", "dataset:stsb_multi_mt", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
hugorosen
null
hugorosen/flaubert_base_uncased-xnli-sts
56
null
sentence-transformers
5,787
--- pipeline_tag: sentence-similarity language: fr tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - fr datasets: - xnli - stsb_multi_mt --- # hugorosen/flaubert_base_uncased-xnli-sts This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"] model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts') model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> This model scores 76.9% on STS test (french) ## Training ### Pre-training We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french). Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: FlaubertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Fine-tuned for semantic similarity by Hugo Rosenkranz-costa. Based on FlauBERT: ``` @InProceedings{le2020flaubert, author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier}, title = {FlauBERT: Unsupervised Language Model Pre-training for French}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {2479--2490}, url = {https://www.aclweb.org/anthology/2020.lrec-1.302} } ```
jegormeister/bert-base-dutch-cased-snli
d507d8dc67f5c40103aeebfd8552595ad806484d
2021-08-16T09:10:25.000Z
[ "pytorch", "bert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
jegormeister
null
jegormeister/bert-base-dutch-cased-snli
56
1
sentence-transformers
5,788
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # bert-base-dutch-cased-snli This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('bert-base-dutch-cased-snli') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('bert-base-dutch-cased-snli') model = AutoModel.from_pretrained('bert-base-dutch-cased-snli') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=bert-base-dutch-cased-snli) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4807 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 0, "evaluator": "utils.CombEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 722, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lamhieu/distilbert-base-multilingual-cased-vietnamese-topicifier
7b31114c03220acb89e26d7a0d20b051404cc713
2021-04-29T18:01:33.000Z
[ "pytorch", "distilbert", "text-classification", "vi", "transformers", "vietnamese", "topicifier", "multilingual", "tiny", "license:mit" ]
text-classification
false
lamhieu
null
lamhieu/distilbert-base-multilingual-cased-vietnamese-topicifier
56
null
transformers
5,789
--- language: - vi tags: - vietnamese - topicifier - multilingual - tiny license: - mit pipeline_tag: text-classification widget: - text: "Đam mê của tôi là nhiếp ảnh" --- # distilbert-base-multilingual-cased-vietnamese-topicifier ## About Fine-tuning from `distilbert-base-multilingual-cased` with a tiny dataset about Vietnamese topics. ## Usage Try entering a message to predict what topic is being discussed. For example: ``` # Photography Đam mê của tôi là nhiếp ảnh # World War I Bạn đã từng nghe về cuộc đại thế chiến ? ``` ## Other The model was fine-tuning with a tiny dataset, don't use it for a product.
mbien/wav2vec2-large-xlsr-polish
5802268ce272423e6772368bd971c5a5104f3c6f
2021-07-06T12:38:34.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "pl", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
mbien
null
mbien/wav2vec2-large-xlsr-polish
56
null
transformers
5,790
--- language: pl datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: mbien/wav2vec2-large-xlsr-polish results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice pl type: common_voice args: pl metrics: - name: Test WER type: wer value: 23.01 --- # Wav2Vec2-Large-XLSR-53-Polish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pl", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("mbien/wav2vec2-large-xlsr-polish") model = Wav2Vec2ForCTC.from_pretrained("mbien/wav2vec2-large-xlsr-polish") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Polish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pl", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("mbien/wav2vec2-large-xlsr-polish") model = Wav2Vec2ForCTC.from_pretrained("mbien/wav2vec2-large-xlsr-polish") model.to("cuda") chars_to_ignore_regex = '[\—\…\,\?\.\!\-\;\:\"\“\„\%\‘\”\�\«\»\'\’]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 23.01 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1DvrFMoKp9h3zk_eXrJF2s4_TGDHh0tMc?usp=sharing)
verloop/Hinglish-Bert
12bc17d68b1a41fe2c7a0897c25573cdd77c69e8
2021-05-20T08:58:33.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
verloop
null
verloop/Hinglish-Bert
56
null
transformers
5,791
Entry not found
yazdipour/text-to-sparql-t5-base
7d5a294a801b6edfcadbf5118e7629ef9e5ad1ed
2021-10-19T18:16:39.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/text-to-sparql-t5-base
56
null
transformers
5,792
--- license: apache-2.0 tags: - generated_from_trainer datasets: - null metrics: - f1 model-index: - name: text-to-sparql-t5-base-2021-10-19_15-35_lastDS results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation metrics: - name: F1 type: f1 value: 0.3275993764400482 --- <!-- 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. --> # text-to-sparql-t5-base-2021-10-19_15-35_lastDS This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1310 - Gen Len: 19.0 - P: 0.5807 - R: 0.0962 - F1: 0.3276 - Score: 6.4533 - Bleu-precisions: [92.48113990507008, 85.38781447185119, 80.57856404313097, 77.37314727416516] - Bleu-bp: 0.0770 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:----------------------------------------------------------------------------:|:-------:| | nan | 1.0 | 4807 | 0.1310 | 19.0 | 0.5807 | 0.0962 | 0.3276 | 6.4533 | [92.48113990507008, 85.38781447185119, 80.57856404313097, 77.37314727416516] | 0.0770 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
malmarjeh/transformer
bfc8db3880ac0abc7d947adba8177a7a410270d0
2022-06-29T13:49:39.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "ar", "transformers", "Transformer", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing", "autotrain_compatible" ]
text2text-generation
false
malmarjeh
null
malmarjeh/transformer
56
null
transformers
5,793
--- language: - ar tags: - Transformer - MSA - Arabic Text Summarization - Arabic News Title Generation - Arabic Paraphrasing widget: - text: "شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من الطرفين." --- # An Arabic abstractive text summarization model A Transformer-based encoder-decoder model which has been trained on a dataset of 384,764 paragraph-summary pairs. More details on the training of this model will be released later. The model can be used as follows: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline from arabert.preprocess import ArabertPreprocessor model_name="malmarjeh/transformer" preprocessor = ArabertPreprocessor(model_name="") tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer) text = "شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من الطرفين." text = preprocessor.preprocess(text) result = pipeline(text, pad_token_id=tokenizer.eos_token_id, num_beams=3, repetition_penalty=3.0, max_length=200, length_penalty=1.0, no_repeat_ngram_size = 3)[0]['generated_text'] result >>> 'احتجاجات شعبية في طرابلس لليوم الثالث على التوالي' ``` ## Contact: **Mohammad Bani Almarjeh**: [Linkedin](https://www.linkedin.com/in/mohammad-bani-almarjeh/) | <[email protected]>
Marxav/frpron
417c1c39b1b9ef2a8dd4cd44cd6b64a2cd5a3d67
2022-07-26T11:57:55.000Z
[ "pytorch", "gpt2", "text-generation", "fr", "dataset:Marxav/frpron", "transformers" ]
text-generation
false
Marxav
null
Marxav/frpron
56
1
transformers
5,794
--- language: - fr thumbnail: "url to a thumbnail used in social sharing" tags: - text-generation datasets: - Marxav/frpron metrics: - loss/eval - perplexity widget: - text: "bonjour:" - text: "salut, comment ça va:" - text: "Louis XIII:" - text: "anticonstitutionnellement:" - text: "les animaux:" inference: parameters: temperature: 0.01 return_full_text: True --- # Fr-word to phonemic pronunciation This model aims at predicting the syllabized phonemic pronunciation of the French words. The generated pronunciation is: * A text string made of International Phonetic Alphabet (IPA) characters; * Phonemic (i.e. remains at the phoneme-level, not deeper); * Syllabized (i.e. characters '.' and '‿' are used to identify syllabes). Such pronunciation is used in the [French Wiktionary](https://fr.wiktionary.org/) in the {{pron|...|fr}} tag. To use this model, simply give an input containing the word that you want to translate followed by ":", for example: "bonjour:". It will generate its predicted pronunciation, for example "bɔ̃.ʒuʁ". This model remains experimental. Additional finetuning is needed for: * [Homographs with different pronunciations](https://fr.wiktionary.org/wiki/Catégorie:Homographes_non_homophones_en_français), * [French liaisons](https://en.wikipedia.org/wiki/Liaison_(French)), * [Roman numerals](https://en.wikipedia.org/wiki/Roman_numerals). The input length is currently limited to a maximum of 60 letters. This work is derived from the [OTEANN paper](https://aclanthology.org/2021.sigtyp-1.1/) and [code](https://github.com/marxav/oteann3), which used [minGTP](https://github.com/karpathy/minGPT). ## More information on the model, dataset, hardware, environmental consideration: ### **The training data** The dataset used for training this models comes from data of the [French Wiktionary](https://fr.wiktionary.org/). ### **The model** The model is build on [gpt2](https://huggingface.co/gpt2)
Helsinki-NLP/opus-mt-tc-big-gmq-en
eaa0e23392aded6a8a207d4522224a95532db343
2022-06-01T12:59:50.000Z
[ "pytorch", "marian", "text2text-generation", "tc", "big", "gmq", "en", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tc-big-gmq-en
56
null
transformers
5,795
--- language: - da - en - fo - gmq - is - nb - nn - false - sv tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-gmq-en results: - task: name: Translation dan-eng type: translation args: dan-eng dataset: name: flores101-devtest type: flores_101 args: dan eng devtest metrics: - name: BLEU type: bleu value: 49.3 - task: name: Translation isl-eng type: translation args: isl-eng dataset: name: flores101-devtest type: flores_101 args: isl eng devtest metrics: - name: BLEU type: bleu value: 34.2 - task: name: Translation nob-eng type: translation args: nob-eng dataset: name: flores101-devtest type: flores_101 args: nob eng devtest metrics: - name: BLEU type: bleu value: 44.2 - task: name: Translation swe-eng type: translation args: swe-eng dataset: name: flores101-devtest type: flores_101 args: swe eng devtest metrics: - name: BLEU type: bleu value: 49.8 - task: name: Translation isl-eng type: translation args: isl-eng dataset: name: newsdev2021.is-en type: newsdev2021.is-en args: isl-eng metrics: - name: BLEU type: bleu value: 30.4 - task: name: Translation dan-eng type: translation args: dan-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: dan-eng metrics: - name: BLEU type: bleu value: 65.9 - task: name: Translation fao-eng type: translation args: fao-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fao-eng metrics: - name: BLEU type: bleu value: 30.1 - task: name: Translation isl-eng type: translation args: isl-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: isl-eng metrics: - name: BLEU type: bleu value: 53.3 - task: name: Translation nno-eng type: translation args: nno-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nno-eng metrics: - name: BLEU type: bleu value: 56.1 - task: name: Translation nob-eng type: translation args: nob-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: nob-eng metrics: - name: BLEU type: bleu value: 60.2 - task: name: Translation swe-eng type: translation args: swe-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: swe-eng metrics: - name: BLEU type: bleu value: 66.4 - task: name: Translation isl-eng type: translation args: isl-eng dataset: name: newstest2021.is-en type: wmt-2021-news args: isl-eng metrics: - name: BLEU type: bleu value: 34.4 --- # opus-mt-tc-big-gmq-en Neural machine translation model for translating from North Germanic languages (gmq) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-09 * source language(s): dan fao isl nno nob nor swe * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip) * more information released models: [OPUS-MT gmq-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Han var synligt nervøs.", "Inte ens Tom själv var övertygad." ] model_name = "pytorch-models/opus-mt-tc-big-gmq-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # He was visibly nervous. # Even Tom was not convinced. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-en") print(pipe("Han var synligt nervøs.")) # expected output: He was visibly nervous. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | dan-eng | tatoeba-test-v2021-08-07 | 0.78292 | 65.9 | 10795 | 79684 | | fao-eng | tatoeba-test-v2021-08-07 | 0.47467 | 30.1 | 294 | 1984 | | isl-eng | tatoeba-test-v2021-08-07 | 0.68346 | 53.3 | 2503 | 19788 | | nno-eng | tatoeba-test-v2021-08-07 | 0.69788 | 56.1 | 460 | 3524 | | nob-eng | tatoeba-test-v2021-08-07 | 0.73524 | 60.2 | 4539 | 36823 | | swe-eng | tatoeba-test-v2021-08-07 | 0.77665 | 66.4 | 10362 | 68513 | | dan-eng | flores101-devtest | 0.72322 | 49.3 | 1012 | 24721 | | isl-eng | flores101-devtest | 0.59616 | 34.2 | 1012 | 24721 | | nob-eng | flores101-devtest | 0.68224 | 44.2 | 1012 | 24721 | | swe-eng | flores101-devtest | 0.72042 | 49.8 | 1012 | 24721 | | isl-eng | newsdev2021.is-en | 0.56709 | 30.4 | 2004 | 46383 | | isl-eng | newstest2021.is-en | 0.57756 | 34.4 | 1000 | 22529 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 19:13:11 EEST 2022 * port machine: LM0-400-22516.local
facebook/wav2vec2-conformer-rope-large-100h-ft
6750d7e0a42c140730f9a5ad15eb1fd865fb51a6
2022-06-15T08:16:47.000Z
[ "pytorch", "wav2vec2-conformer", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2010.05171", "transformers", "speech", "audio", "hf-asr-leaderboard", "license:apache-2.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-conformer-rope-large-100h-ft
56
null
transformers
5,796
--- language: en datasets: - librispeech_asr tags: - speech - audio - automatic-speech-recognition - hf-asr-leaderboard license: apache-2.0 --- # Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) **Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171). The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft") model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ```
Evelyn18/distilbert-base-uncased-finetuned-squad
229e1dfe28d7fb0d369d100a2101f1204c27f34c
2022-06-22T03:50:33.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:becasv2", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
Evelyn18
null
Evelyn18/distilbert-base-uncased-finetuned-squad
56
null
transformers
5,797
--- license: apache-2.0 tags: - generated_from_trainer datasets: - becasv2 model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 4.0087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.5219 | | No log | 2.0 | 10 | 4.9747 | | No log | 3.0 | 15 | 4.5448 | | No log | 4.0 | 20 | 4.1843 | | No log | 5.0 | 25 | 3.8491 | | No log | 6.0 | 30 | 3.6789 | | No log | 7.0 | 35 | 3.5018 | | No log | 8.0 | 40 | 3.4254 | | No log | 9.0 | 45 | 3.4566 | | No log | 10.0 | 50 | 3.4326 | | No log | 11.0 | 55 | 3.5741 | | No log | 12.0 | 60 | 3.5260 | | No log | 13.0 | 65 | 3.7003 | | No log | 14.0 | 70 | 3.7499 | | No log | 15.0 | 75 | 3.7961 | | No log | 16.0 | 80 | 3.8578 | | No log | 17.0 | 85 | 3.9928 | | No log | 18.0 | 90 | 4.0305 | | No log | 19.0 | 95 | 4.0024 | | No log | 20.0 | 100 | 4.0087 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
nielsr/layoutlmv3-funsd-v2
301f7a9dc1416e286cb7219aff24630b06d0c09a
2022-05-11T16:06:54.000Z
[ "pytorch", "layoutlmv3", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
nielsr
null
nielsr/layoutlmv3-funsd-v2
56
1
transformers
5,798
Entry not found
emilylearning/cond_ft_birth_date_on_wiki_bio__prcnt_100__test_run_False
70534a6aed98ce4575cf9f79fd2af024b3e24c00
2022-05-12T23:38:56.000Z
[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
emilylearning
null
emilylearning/cond_ft_birth_date_on_wiki_bio__prcnt_100__test_run_False
56
null
transformers
5,799
Entry not found