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train · 872k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
milyiyo/distilbert-base-uncased-finetuned-amazon-review	milyiyo	2022-01-20T15:14:48Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-base-uncased-finetuned-amazon-review results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: es metrics: - name: Accuracy type: accuracy value: 0.693 - name: F1 type: f1 value: 0.7002653469272611 - name: Precision type: precision value: 0.709541681233075 - name: Recall type: recall value: 0.693 --- <!-- 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-amazon-review This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.3494 - Accuracy: 0.693 - F1: 0.7003 - Precision: 0.7095 - Recall: 0.693 ## 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 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 0.5 \| 500 \| 0.8287 \| 0.7104 \| 0.7120 \| 0.7152 \| 0.7104 \| \| 0.4238 \| 1.0 \| 1000 \| 0.8917 \| 0.7094 \| 0.6989 \| 0.6917 \| 0.7094 \| \| 0.4238 \| 1.5 \| 1500 \| 0.9367 \| 0.6884 \| 0.6983 \| 0.7151 \| 0.6884 \| \| 0.3152 \| 2.0 \| 2000 \| 0.9845 \| 0.7116 \| 0.7144 \| 0.7176 \| 0.7116 \| \| 0.3152 \| 2.5 \| 2500 \| 1.0752 \| 0.6814 \| 0.6968 \| 0.7232 \| 0.6814 \| \| 0.2454 \| 3.0 \| 3000 \| 1.1215 \| 0.6918 \| 0.6954 \| 0.7068 \| 0.6918 \| \| 0.2454 \| 3.5 \| 3500 \| 1.2905 \| 0.6976 \| 0.7048 \| 0.7138 \| 0.6976 \| \| 0.1989 \| 4.0 \| 4000 \| 1.2938 \| 0.694 \| 0.7016 \| 0.7113 \| 0.694 \| \| 0.1989 \| 4.5 \| 4500 \| 1.3623 \| 0.6972 \| 0.7014 \| 0.7062 \| 0.6972 \| \| 0.1746 \| 5.0 \| 5000 \| 1.3494 \| 0.693 \| 0.7003 \| 0.7095 \| 0.693 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Mirjam/test-finetuned	Mirjam	2022-01-20T15:14:18Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: test-finetuned 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. --> # test-finetuned This model is a fine-tuned version of [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 1 \| nan \| 33.8462 \| 31.746 \| 30.7692 \| 30.7692 \| 86.0 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.15.1 - Tokenizers 0.10.3
pitehu/T5_NER_CONLL_LIST	pitehu	2022-01-20T14:32:20Z	12	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "Named Entity Recognition", "en", "dataset:wmt19", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - en tags: - Named Entity Recognition license: apache-2.0 datasets: - wmt19 metrics: - bleu - sacrebleu inference: parameters: max_length: 1024 ---
hrdipto/wav2vec2-xls-r-tf-left-right-shuru	hrdipto	2022-01-20T08:48:17Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-tf-left-right-shuru 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. --> # wav2vec2-xls-r-tf-left-right-shuru This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0921 - Wer: 1.2628 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 6.5528 \| 23.81 \| 500 \| 0.5509 \| 1.9487 \| \| 0.2926 \| 47.62 \| 1000 \| 0.1306 \| 1.2756 \| \| 0.1171 \| 71.43 \| 1500 \| 0.1189 \| 1.2628 \| \| 0.0681 \| 95.24 \| 2000 \| 0.0921 \| 1.2628 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
D3xter1922/electra-base-discriminator-finetuned-cola	D3xter1922	2022-01-20T01:03:51Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: electra-base-discriminator-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.6824089073723449 --- <!-- 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. --> # electra-base-discriminator-finetuned-cola This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6367 - Matthews Correlation: 0.6824 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4139 \| 1.0 \| 535 \| 0.4137 \| 0.6381 \| \| 0.2452 \| 2.0 \| 1070 \| 0.4887 \| 0.6504 \| \| 0.17 \| 3.0 \| 1605 \| 0.5335 \| 0.6757 \| \| 0.1135 \| 4.0 \| 2140 \| 0.6367 \| 0.6824 \| \| 0.0817 \| 5.0 \| 2675 \| 0.6742 \| 0.6755 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
nimrah/wav2vec2-large-xls-r-300m-hindi-colab	nimrah	2022-01-19T21:21:34Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-colab 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. --> # wav2vec2-large-xls-r-300m-hindi-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
UBC-NLP/ARBERT	UBC-NLP	2022-01-19T20:10:55Z	540	5	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "Arabic BERT", "MSA", "Twitter", "Masked Langauge Model", "ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: - ar tags: - Arabic BERT - MSA - Twitter - Masked Langauge Model widget: - text: "اللغة العربية هي لغة [MASK]." --- <img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/> ARBERT is one of three models described in our ACl 2021 paper ["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://mageed.arts.ubc.ca/files/2020/12/marbert_arxiv_2020.pdf). ARBERT is a large-scale pre-trained masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use the same architecture as BERT-base: 12 attention layers, each has 12 attention heads and 768 hidden dimensions, a vocabulary of 100K WordPieces, making ∼163M parameters. We train ARBERT on a collection of Arabic datasets comprising 61GB of text (6.2B tokens). For more information, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert). # BibTex If you use our models (ARBERT, MARBERT, or MARBERTv2) 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{abdul-mageed-etal-2021-arbert, title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic", author = "Abdul-Mageed, Muhammad and Elmadany, AbdelRahim and Nagoudi, El Moatez Billah", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.551", doi = "10.18653/v1/2021.acl-long.551", pages = "7088--7105", abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.", } ``` ## 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.
hrdipto/wav2vec2-xls-r-tf-left-right-trainer	hrdipto	2022-01-19T20:06:38Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-tf-left-right-trainer 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. --> # wav2vec2-xls-r-tf-left-right-trainer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0090 - eval_wer: 0.0037 - eval_runtime: 11.2686 - eval_samples_per_second: 71.703 - eval_steps_per_second: 8.963 - epoch: 21.05 - step: 4000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
kjackson/distilbert-base-uncased-finetuned-emotion	kjackson	2022-01-19T19:10:27Z	0	0	null	[ "exbert", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1907.11692", "license:mit", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en tags: - exbert license: mit datasets: - bookcorpus - wikipedia --- # RoBERTa base model Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it makes a difference between english and English. Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.
vuiseng9/bert-base-squadv1	vuiseng9	2022-01-19T19:03:57Z	5	0	transformers	[ "transformers", "pytorch", "onnx", "bert", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	This model is a fork of [```csarron/bert-base-uncased-squad-v1```](https://huggingface.co/csarron/bert-base-uncased-squad-v1). ``` eval_exact_match = 80.9082 eval_f1 = 88.2275 eval_samples = 10784 ``` # Eval ```bash export CUDA_VISIBLE_DEVICES=0 OUTDIR=eval-bert-base-squadv1 WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path vuiseng9/bert-base-squadv1 \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 128 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 \| tee $OUTDIR/run.log & ```
huggingtweets/t_zahil	huggingtweets	2022-01-19T16:50:12Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- 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('https://pbs.twimg.com/profile_images/1374040164180299791/ACw4G3nZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Thomas Sanlis 🌱</div> <div style="text-align: center; font-size: 14px;">@t_zahil</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 Thomas Sanlis 🌱. \| Data \| Thomas Sanlis 🌱 \| \| --- \| --- \| \| Tweets downloaded \| 3242 \| \| Retweets \| 597 \| \| Short tweets \| 312 \| \| Tweets kept \| 2333 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33umauvo/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 @t_zahil's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fhm3dlx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fhm3dlx/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/t_zahil') 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)
dehio/german-qg-t5-drink600	dehio	2022-01-19T16:38:22Z	7	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: mit widget: - text: "generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert." language: - de tags: - question generation datasets: - deepset/germanquad model-index: - name: german-qg-t5-drink600 results: [] --- # german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions. ## Task example #### Input generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert. #### Expected Question Zu welchen Gelegenheiten passt der Monk Sour gut? ## Model description The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). We have not yet open sourced the dataset, since we do not own copyright on the source material. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ## Evaluation It achieves a BLEU-4 score of 29.80 on the drink600 test set (n=120) and 11.30 on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, german-qg-t5-quad achieves a BLEU-4 score of 10.76 on the drink600 test set. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
dehio/german-qg-t5-quad	dehio	2022-01-19T16:36:25Z	12	5	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "question generation", "de", "dataset:deepset/germanquad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: mit widget: - text: "Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl>britischen Common Laws<hl> sind, setzt sich das amerikanische Recht bedeutend davon ab." language: - de tags: - question generation datasets: - deepset/germanquad model-index: - name: german-qg-t5-quad results: [] --- # german-qg-t5-quad This model is fine-tuned in question generation in German. The expected answer must be highlighted with a <hl> token. ## Task example #### Input generate question: Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des <hl> britischen Common Laws <hl> sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, [...] #### Expected output Von welchem Gesetzt stammt das Amerikanische ab? ## Model description This model is a fine-tuned version of [valhalla/t5-base-qg-hl](https://huggingface.co/valhalla/t5-base-qg-hl) on the [GermanQUAD](https://www.deepset.ai/germanquad) dataset. ## Training and evaluation data The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg). ### Evaluation The model achieves a BLEU-4 score of 11.30 on the GermanQuAD test set (n=2204). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
indonesian-nlp/wav2vec2-luganda	indonesian-nlp	2022-01-19T16:19:45Z	11	2	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "lg", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: lg datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech license: apache-2.0 model-index: - name: Wav2Vec2 Luganda by Indonesian-NLP results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice lg type: common_voice args: lg metrics: - name: Test WER type: wer value: 7.53 --- # Automatic Speech Recognition for Luganda This is the model built for the [Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition). It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0. We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model. 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", "lg", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") 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): if "audio" in batch: speech_array = torch.tensor(batch["audio"]["array"]) else: 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[: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 Indonesian 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", "lg", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") model.to("cuda") chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"] chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() if "audio" in batch: speech_array = torch.tensor(batch["audio"]["array"]) else: 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 audio 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"]))) ``` WER without KenLM: 15.38 % WER With KenLM: Test Result: 7.53 % ## Training The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
mishig/test_vid	mishig	2022-01-19T09:56:39Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# Video demo on ModelCard Please find [this file](https://huggingface.co/mishig/test_vid/blob/main/README.md) to see how to add a video to model card. <video src="https://huggingface.co/mishig/test_vid/resolve/main/output.mp4" controls autoplay loop/>
mrp/distilbert-base-uncased-finetuned-imdb	mrp	2022-01-19T08:44:09Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4718 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.707 \| 1.0 \| 157 \| 2.4883 \| \| 2.572 \| 2.0 \| 314 \| 2.4240 \| \| 2.5377 \| 3.0 \| 471 \| 2.4355 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
AT/distilroberta-base-finetuned-wikitext2	AT	2022-01-19T08:22:36Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 80.0 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/wmascen	huggingtweets	2022-01-19T04:52:23Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/wmascen/1642567908765/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('https://pbs.twimg.com/profile_images/1453179488569802752/LsB82o0-_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">wihrel</div> <div style="text-align: center; font-size: 14px;">@wmascen</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 wihrel. \| Data \| wihrel \| \| --- \| --- \| \| Tweets downloaded \| 2900 \| \| Retweets \| 203 \| \| Short tweets \| 236 \| \| Tweets kept \| 2461 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bsbw98xm/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 @wmascen's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pwlitks) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pwlitks/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/wmascen') 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)
chitra/finetune-paraphrase-model	chitra	2022-01-19T04:40:57Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model-index: - name: finetune-paraphrase-model 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. --> # finetune-paraphrase-model This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.1 \| 200 \| 3.0116 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
NbAiLab/roberta_des_128	NbAiLab	2022-01-19T01:06:51Z	3	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use. This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why. Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs here though. Changing the corpus and continue training.
malloc/OpenNMT-py-English-German-Transformer	malloc	2022-01-18T20:18:11Z	0	2	null	[ "translation", "pytorch", "de", "en", "dataset:WMT", "license:mit", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - de - en tags: - translation - pytorch license: mit datasets: - WMT metrics: - bleu --- # OpenNMT-py-English-German-Transformer [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for English to German translation. - Configuration: Base Transformer configuration with [standard training options](http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-the-transformer-model-do-you-support-multi-gpu) - Data: WMT with shared SentencePiece model - BLEU: - newstest2014 = 26.89 - newstest2017 = 28.09
vuiseng9/bert-base-squadv1-pruneofa-90pc-bt	vuiseng9	2022-01-18T19:13:21Z	3	0	transformers	[ "transformers", "pytorch", "onnx", "bert", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset. ``` eval_exact_match = 80.2933 eval_f1 = 87.6788 eval_samples = 10784 ``` # Train use https://github.com/IntelLabs/Model-Compression-Research-Package.git see ```pruneofa-transfer-learning.sh``` # Eval ```bash export CUDA_VISIBLE_DEVICES=0 OUTDIR=eval-bert-base-squadv1-pruneofa-90pc-bt WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 128 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 \| tee $OUTDIR/run.log & ```
milyiyo/electra-small-finetuned-amazon-review	milyiyo	2022-01-18T17:47:17Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 - precision - recall model-index: - name: electra-small-finetuned-amazon-review results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: en metrics: - name: Accuracy type: accuracy value: 0.5504 - name: F1 type: f1 value: 0.5457527808330634 - name: Precision type: precision value: 0.5428695841337288 - name: Recall type: recall value: 0.5504 --- <!-- 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. --> # electra-small-finetuned-amazon-review This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.0560 - Accuracy: 0.5504 - F1: 0.5458 - Precision: 0.5429 - Recall: 0.5504 ## 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: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.2172 \| 1.0 \| 1000 \| 1.1014 \| 0.5216 \| 0.4902 \| 0.4954 \| 0.5216 \| \| 1.0027 \| 2.0 \| 2000 \| 1.0388 \| 0.549 \| 0.5471 \| 0.5494 \| 0.549 \| \| 0.9035 \| 3.0 \| 3000 \| 1.0560 \| 0.5504 \| 0.5458 \| 0.5429 \| 0.5504 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/collision	huggingtweets	2022-01-18T17:17:28Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/collision/1642526243846/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('https://pbs.twimg.com/profile_images/2464132281/jbbxl9p7ratdyuposrif_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">John Collison</div> <div style="text-align: center; font-size: 14px;">@collision</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 John Collison. \| Data \| John Collison \| \| --- \| --- \| \| Tweets downloaded \| 3222 \| \| Retweets \| 999 \| \| Short tweets \| 206 \| \| Tweets kept \| 2017 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ifqwdbm/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 @collision's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3/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/collision') 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)
tal-yifat/injury-report-test	tal-yifat	2022-01-18T16:24:00Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: injury-report-test 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. --> # injury-report-test 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: 1.5697 ## 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: 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.8158 \| 1.0 \| 6633 \| 1.7368 \| \| 1.6984 \| 2.0 \| 13266 \| 1.6198 \| \| 1.6209 \| 3.0 \| 19899 \| 1.5800 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
phueb/BabyBERTa-3	phueb	2022-01-18T14:41:25Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "BabyBERTa", "en", "dataset:CHILDES", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: en tags: - BabyBERTa license: mit datasets: - CHILDES widget: - text: "Look here. What is that <mask> ?" - text: "Do you like your <mask> ?" --- ## BabyBERTA ### Overview BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input. It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed. The three provided models are randomly selected from 10 that were trained and reported in the paper. ## Loading the tokenizer BabyBERTa was trained with `add_prefix_space=True`, so it will not work properly with the tokenizer defaults. For instance, to load the tokenizer for BabyBERTa-1, load it as follows: ```python tokenizer = RobertaTokenizerFast.from_pretrained("phueb/BabyBERTa-1", add_prefix_space=True) ``` ### Hyper-Parameters See the paper for details. All provided models were trained for 400K steps with a batch size of 16. Importantly, BabyBERTa never predicts unmasked tokens during training - `unmask_prob` is set to zero. ### Performance BabyBerta was developed for learning grammatical knowledge from child-directed input. Its grammatical knowledge was evaluated using the [Zorro](https://github.com/phueb/Zorro) test suite. The best model achieves an overall accuracy of 80.3, comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021). Both values differ slightly from those reported in the [CoNLL 2021 paper](https://aclanthology.org/2021.conll-1.49/). There are two reasons for this: 1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation. Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased. In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change. 2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective. this resulted in a small reduction in the performance of BabyBERTa. Overall Accuracy on Zorro: \| Model Name \| Accuracy (holistic scoring) \| Accuracy (MLM-scoring) \| \|----------------------------------------\|------------------------------\|------------\| \| [BabyBERTa-1][link-BabyBERTa-1] \| 80.3 \| 79.9 \| \| [BabyBERTa-2][link-BabyBERTa-2] \| 78.6 \| 78.2 \| \| [BabyBERTa-3][link-BabyBERTa-3] \| 74.5 \| 78.1 \| ### Additional Information This model was trained by [Philip Huebner](https://philhuebner.com), currently at the [UIUC Language and Learning Lab](http://www.learninglanguagelab.org). More info can be found [here](https://github.com/phueb/BabyBERTa). [link-BabyBERTa-1]: https://huggingface.co/phueb/BabyBERTa-1 [link-BabyBERTa-2]: https://huggingface.co/phueb/BabyBERTa-2 [link-BabyBERTa-3]: https://huggingface.co/phueb/BabyBERTa-3
NbAiLab/roberta_des_ada_128_6e4	NbAiLab	2022-01-18T10:45:01Z	8	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
huggingtweets/dankogai-hirox246	huggingtweets	2022-01-18T09:55:05Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/dankogai-hirox246/1642499700234/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('https://pbs.twimg.com/profile_images/646595746905620480/oeKI14gB_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1190142566831984640/o4kO2hp-_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">ひろゆき, Hiroyuki Nishimura & Dan Kogai</div> <div style="text-align: center; font-size: 14px;">@dankogai-hirox246</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 ひろゆき, Hiroyuki Nishimura & Dan Kogai. \| Data \| ひろゆき, Hiroyuki Nishimura \| Dan Kogai \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3249 \| 3250 \| \| Retweets \| 284 \| 340 \| \| Short tweets \| 1988 \| 2416 \| \| Tweets kept \| 977 \| 494 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vrtv6xf/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 @dankogai-hirox246's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1yfxplpr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1yfxplpr/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/dankogai-hirox246') 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)
hkunlp/T5_large_prefix_all_tasks_2upsample2	hkunlp	2022-01-18T07:15:22Z	4	2	transformers	[ "transformers", "pytorch", "t5", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2. Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
dmiller1/distilbert-base-uncased-finetuned-emotion	dmiller1	2022-01-18T03:59:30Z	5	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.926 - name: F1 type: f1 value: 0.9261144741040841 --- <!-- 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-emotion 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: 0.2161 - Accuracy: 0.926 - F1: 0.9261 ## 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: 64 - eval_batch_size: 64 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8436 \| 1.0 \| 250 \| 0.3175 \| 0.9105 \| 0.9081 \| \| 0.2492 \| 2.0 \| 500 \| 0.2161 \| 0.926 \| 0.9261 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.7.1 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/dankogai-hirox246-syakkin_dama	huggingtweets	2022-01-18T02:01:17Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/dankogai-hirox246-syakkin_dama/1642471272927/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('https://pbs.twimg.com/profile_images/646595746905620480/oeKI14gB_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1190142566831984640/o4kO2hp-_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1283621672541536259/WI_8OTJz_400x400.jpg')"> </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">ひろゆき, Hiroyuki Nishimura & Dan Kogai & 借金玉</div> <div style="text-align: center; font-size: 14px;">@dankogai-hirox246-syakkin_dama</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 ひろゆき, Hiroyuki Nishimura & Dan Kogai & 借金玉. \| Data \| ひろゆき, Hiroyuki Nishimura \| Dan Kogai \| 借金玉 \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3249 \| 3250 \| 3249 \| \| Retweets \| 283 \| 341 \| 260 \| \| Short tweets \| 1819 \| 2313 \| 2918 \| \| Tweets kept \| 1147 \| 596 \| 71 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1meoqt2b/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 @dankogai-hirox246-syakkin_dama's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gc1ic0l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gc1ic0l/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/dankogai-hirox246-syakkin_dama') 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/eri_razapii-hayakawagomi-nagiko726	huggingtweets	2022-01-18T01:03:14Z	0	0	null	[ "huggingtweets", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/eri_razapii-hayakawagomi-nagiko726/1642467789468/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('https://pbs.twimg.com/profile_images/1242278691494756352/TfHYNcpA_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1463699400405164034/aRY9jlnO_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1087144695568855041/p7u3lvnC_400x400.jpg')"> </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">Nagisa Ichikawa 🧠 THE GUILD & えりらざぴ \| SHE CEO/CCO & ハヤカワ五味</div> <div style="text-align: center; font-size: 14px;">@eri_razapii-hayakawagomi-nagiko726</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 Nagisa Ichikawa 🧠 THE GUILD & えりらざぴ \| SHE CEO/CCO & ハヤカワ五味. \| Data \| Nagisa Ichikawa 🧠 THE GUILD \| えりらざぴ \| SHE CEO/CCO \| ハヤカワ五味 \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3236 \| 3234 \| 3250 \| \| Retweets \| 846 \| 1768 \| 175 \| \| Short tweets \| 1733 \| 1185 \| 2943 \| \| Tweets kept \| 657 \| 281 \| 132 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wxptdvg/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 @eri_razapii-hayakawagomi-nagiko726's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1g5vtvdk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1g5vtvdk/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/eri_razapii-hayakawagomi-nagiko726') 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)
ronanki/xlmr_17-01-2022_v3	ronanki	2022-01-17T20:34:20Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/xlmr_17-01-2022_v3 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('ronanki/xlmr_17-01-2022_v3') 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('ronanki/xlmr_17-01-2022_v3') model = AutoModel.from_pretrained('ronanki/xlmr_17-01-2022_v3') # 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 --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/xlmr_17-01-2022_v3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
dshvadskiy/bert-finetuned-ner	dshvadskiy	2022-01-17T17:54:13Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2002", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2002 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2002 type: conll2002 args: es metrics: - name: Precision type: precision value: 0.7394396551724138 - name: Recall type: recall value: 0.7883731617647058 - name: F1 type: f1 value: 0.7631227758007118 - name: Accuracy type: accuracy value: 0.9655744705631151 --- <!-- 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-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset. It achieves the following results on the evaluation set: - Loss: 0.1458 - Precision: 0.7394 - Recall: 0.7884 - F1: 0.7631 - Accuracy: 0.9656 ## 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: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1047 \| 1.0 \| 1041 \| 0.1516 \| 0.7173 \| 0.7505 \| 0.7335 \| 0.9602 \| \| 0.068 \| 2.0 \| 2082 \| 0.1280 \| 0.7470 \| 0.7888 \| 0.7673 \| 0.9664 \| \| 0.0406 \| 3.0 \| 3123 \| 0.1458 \| 0.7394 \| 0.7884 \| 0.7631 \| 0.9656 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
jiobiala24/wav2vec2-base-checkpoint-6	jiobiala24	2022-01-17T14:22:20Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-6 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. --> # wav2vec2-base-checkpoint-6 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-5](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-5) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9738 - Wer: 0.3323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.3435 \| 1.82 \| 1000 \| 0.5637 \| 0.3419 \| \| 0.2599 \| 3.65 \| 2000 \| 0.5804 \| 0.3473 \| \| 0.2043 \| 5.47 \| 3000 \| 0.6481 \| 0.3474 \| \| 0.1651 \| 7.3 \| 4000 \| 0.6937 \| 0.3452 \| \| 0.1376 \| 9.12 \| 5000 \| 0.7221 \| 0.3429 \| \| 0.118 \| 10.95 \| 6000 \| 0.7634 \| 0.3441 \| \| 0.105 \| 12.77 \| 7000 \| 0.7789 \| 0.3444 \| \| 0.0925 \| 14.6 \| 8000 \| 0.8209 \| 0.3444 \| \| 0.0863 \| 16.42 \| 9000 \| 0.8293 \| 0.3440 \| \| 0.0756 \| 18.25 \| 10000 \| 0.8553 \| 0.3412 \| \| 0.0718 \| 20.07 \| 11000 \| 0.9006 \| 0.3430 \| \| 0.0654 \| 21.9 \| 12000 \| 0.9541 \| 0.3458 \| \| 0.0605 \| 23.72 \| 13000 \| 0.9400 \| 0.3350 \| \| 0.0552 \| 25.55 \| 14000 \| 0.9547 \| 0.3363 \| \| 0.0543 \| 27.37 \| 15000 \| 0.9715 \| 0.3348 \| \| 0.0493 \| 29.2 \| 16000 \| 0.9738 \| 0.3323 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
nielsr/tapex-large-finetuned-tabfact	nielsr	2022-01-17T13:39:28Z	5	0	transformers	[ "transformers", "pytorch", "bart", "text-classification", "tapex", "en", "dataset:tab_fact", "arxiv:2107.07653", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: en tags: - tapex license: apache-2.0 datasets: - tab_fact inference: false --- TAPEX-large model fine-tuned on WTQ. This model 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. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining). To load it and run inference, you can do the following: ``` from transformers import BartTokenizer, BartForSequenceClassification import pandas as pd tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-tabfact") model = BartForSequenceClassification.from_pretrained("nielsr/tapex-large-finetuned-tabfact") # create table data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} table = pd.DataFrame.from_dict(data) # turn into dict table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]} # turn into format TAPEX expects # define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) # add sentence sentence = "George Clooney has 69 movies" joint_input = sentence + " " + linear_table # encode encoding = tokenizer(joint_input, return_tensors="pt") # forward pass outputs = model(**encoding) # print prediction logits = outputs.logits print(logits.argmax(-1)) ```
Dumiiii/wav2vec2-xls-r-300m-romanian	Dumiiii	2022-01-17T13:34:59Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-xls-r-300m-romanian --- <!-- 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 achieves WER on common-voice ro test split of WER: 12.457178% # wav2vec2-xls-r-300m-romanian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an common voice ro and RSS dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0836 - eval_wer: 0.0705 - eval_runtime: 160.4549 - eval_samples_per_second: 11.081 - eval_steps_per_second: 1.39 - epoch: 14.38 - step: 2703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 15 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3 Used the following code for evaluation: ``` import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ro", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") model = Wav2Vec2ForCTC.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") model.to("cuda") chars_to_ignore_regex = '['+string.punctuation+']' 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"]))) ``` Credits for evaluation: https://huggingface.co/anton-l
groadabike/ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline	groadabike	2022-01-17T12:53:22Z	11	1	asteroid	[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "license:cc-by-sa-4.0", "region:us" ]	audio-to-audio	2022-03-02T23:29:05Z	--- tags: - asteroid - audio - ConvTasNet - audio-to-audio datasets: - DAMP-VSEP - Singing/Accompaniment Separation license: cc-by-sa-4.0 --- ## Description: This model was trained by Gerardo Roa using the dampvsep recipe in Asteroid. It was trained on the `singing/accompaniment` task of the `DAMP-VSEP` dataset. ## Training config: ```yaml data: channels: 1 emb_model: 'no' metadata_path: metadata mixture: remix root_path: /fastdata/acp13gr/DAMP/DAMP-VSEP sample_rate: 16000 train_set: english_nonenglish filterbank: kernel_size: 20 n_filters: 256 stride: 10 main_args: exp_dir: exp/train_convtasnet_remix-no-0.0-english_nonenglish-0.0005-jade help: null masknet: bn_chan: 256 conv_kernel_size: 3 hid_chan: 512 mask_act: relu n_blocks: 10 n_repeats: 4 n_src: 2 norm_type: gLN skip_chan: 256 optim: lr: 0.0005 optimizer: adam weight_decay: 0.0 positional arguments: {} training: batch_size: 7 early_stop: true epochs: 50 half_lr: true loss_alpha: 0.0 num_workers: 10 ``` ## Results: ```yaml "si_sdr": 15.111802516750586, "si_sdr_imp": 15.178209807687663, "si_sdr_s0": 12.160261214703553, "si_sdr_s0_imp": 17.434593619085675, "si_sdr_s1": 18.063343818797623, "si_sdr_s1_imp": 12.92182599628965, "sdr": 15.959722569460281, "sdr_imp": 14.927002467087567, "sdr_s0": 13.270412028426595, "sdr_s0_imp": 16.45867572657551, "sdr_s1": 18.64903311049397, "sdr_s1_imp": 13.39532920759962, "sir": 23.935932341084754, "sir_imp": 22.903212238712012, "sir_s0": 22.30777879911744, "sir_s0_imp": 25.49604249726635, "sir_s1": 25.56408588305207, "sir_s1_imp": 20.310381980157665, "sar": 17.174899162445882, "sar_imp": -134.47377304178818, "sar_s0": 14.268071153965913, "sar_s0_imp": -137.38060105026818, "sar_s1": 20.081727170925856, "sar_s1_imp": -131.56694503330817, "stoi": 0.7746496376326059, "stoi_imp": 0.19613735629114643, "stoi_s0": 0.6611376621212413, "stoi_s0_imp": 0.21162695175464794, "stoi_s1": 0.8881616131439705, "stoi_s1_imp": 0.1806477608276449 ``` ## License notice: This is important, please fill it, if you need help, you can ask on Asteroid's slack. This work "ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline" is a derivative of [DAMP-VSEP corpus](https://zenodo.org/record/3553059) by [Smule, Inc](https://www.smule.com/), used under [Restricted License](https://zenodo.org/record/3553059)(Research only). "ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Gerardo Roa.
addy88/T5-23-emotions-detections	addy88	2022-01-17T12:08:03Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	### How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("addy88/T5-23-emotions-detections") tokenizer = T5Tokenizer.from_pretrained("addy88/T5-23-emotions-detections") text_to_summarize="emotion: i don't like it this is nonsense." input_ids = tokenizer.encode(text_to_summarize, return_tensors="pt", add_special_tokens=True) input_ids = input_ids.to(self.device) generated_ids = model.generate( input_ids=input_ids, num_beams=2, max_length=512, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True, top_p=0.95, top_k=50, num_return_sequences=1, ) preds = [tokenizer.decode(g,skip_special_tokens=True,clean_up_tokenization_spaces=True,)for g in generated_ids] ```
nielsr/tapex-large-finetuned-wtq	nielsr	2022-01-17T09:56:43Z	8	2	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wtq", "arxiv:2107.07653", "license:apache-2.0", "autotrain_compatible", "region:us" ]	table-question-answering	2022-03-02T23:29:05Z	--- language: en tags: - tapex - table-question-answering license: apache-2.0 datasets: - wtq inference: false --- TAPEX-large model fine-tuned on WTQ. This model 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. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining). To load it and run inference, you can do the following: ``` from transformers import BartTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-wtq") model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large-finetuned-wtq") # create table data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} table = pd.DataFrame.from_dict(data) # turn into dict table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]} # turn into format TAPEX expects # define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) # add question question = "how many movies does George Clooney have?" joint_input = question + " " + linear_table # encode encoding = tokenizer(joint_input, return_tensors="pt") # forward pass outputs = model.generate(**encoding) # decode tokenizer.batch_decode(outputs, skip_special_tokens=True) ```
YasinShihab/asr-en-bn-test	YasinShihab	2022-01-17T06:37:54Z	0	1	null	[ "bn", "audio", "automatic-speech-recognition", "speech", "dataset:OpenSLR", "license:cc-by-sa-4.0", "model-index", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: Bengali datasets: - OpenSLR metrics: - wer tags: - bn - audio - automatic-speech-recognition - speech license: cc-by-sa-4.0 model-index: - name: XLSR Wav2Vec2 Bengali by Arijit results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR type: OpenSLR args: ben metrics: - name: Test WER type: wer value: 32.45 --- # Wav2Vec2-Large-XLSR-Bengali Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training. When using this model, make sure that your speech input is sampled at 16kHz. Train Script can be Found at : train.py Data Prep Notebook : https://colab.research.google.com/drive/1JMlZPU-DrezXjZ2t7sOVqn7CJjZhdK2q?usp=sharing Inference Notebook : https://colab.research.google.com/drive/1uKC2cK9JfUPDTUHbrNdOYqKtNozhxqgZ?usp=sharing ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor processor = Wav2Vec2Processor.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali") model = Wav2Vec2ForCTC.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali") # model = model.to("cuda") resampler = torchaudio.transforms.Resample(TEST_AUDIO_SR, 16_000) def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch) speech = resampler(speech_array).squeeze().numpy() return speech speech_array = speech_file_to_array_fn("test_file.wav") inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values).logits predicted_ids = torch.argmax(logits, dim=-1) preds = processor.batch_decode(predicted_ids)[0] print(preds.replace("[PAD]","")) ``` Test Result: WER on ~4200 utterance : 32.45 %
DoyyingFace/dummy-model	DoyyingFace	2022-01-17T05:44:26Z	6	0	transformers	[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: dummy-model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3
sahri/indonesiasentiment	sahri	2022-01-17T04:50:03Z	19	0	transformers	[ "transformers", "pytorch", "tf", "roberta", "text-classification", "indonesian-roberta-base-sentiment-classifier", "id", "dataset:indonlu", "arxiv:1907.11692", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: id tags: - indonesian-roberta-base-sentiment-classifier license: mit datasets: - indonlu widget: - text: "tidak jelek tapi keren" --- ## Indonesian RoBERTa Base Sentiment Classifier Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indonlu`](https://hf.co/datasets/indonlu)'s `SmSA` dataset consisting of Indonesian comments and reviews. After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%. Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless. ## Model \| Model \| #params \| Arch. \| Training/Validation data (text) \| \| ---------------------------------------------- \| ------- \| ------------ \| ------------------------------- \| \| `indonesian-roberta-base-sentiment-classifier` \| 124M \| RoBERTa Base \| `SmSA` \| ## Evaluation Results The model was trained for 5 epochs and the best model was loaded at the end. \| Epoch \| Training Loss \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \| ----- \| ------------- \| --------------- \| -------- \| -------- \| --------- \| -------- \| \| 1 \| 0.342600 \| 0.213551 \| 0.928571 \| 0.898539 \| 0.909803 \| 0.890694 \| \| 2 \| 0.190700 \| 0.213466 \| 0.934127 \| 0.901135 \| 0.925297 \| 0.882757 \| \| 3 \| 0.125500 \| 0.219539 \| 0.942857 \| 0.920901 \| 0.927511 \| 0.915193 \| \| 4 \| 0.083600 \| 0.235232 \| 0.943651 \| 0.924227 \| 0.926494 \| 0.922048 \| \| 5 \| 0.059200 \| 0.262473 \| 0.942063 \| 0.920583 \| 0.924084 \| 0.917351 \| ## How to Use ### As Text Classifier ```python from transformers import pipeline pretrained_name = "sahri/sentiment" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("tidak jelek tapi keren") ``` ## Disclaimer Do consider the biases which come from both the pre-trained RoBERTa model and the `SmSA` dataset that may be carried over into the results of this model. ## Author Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.
huggingtweets/lazar181	huggingtweets	2022-01-17T01:55:14Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/lazar181/1642384387963/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('https://pbs.twimg.com/profile_images/1451342601483952130/-RJ3Ewqp_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">Ari/Sera @ 🛌</div> <div style="text-align: center; font-size: 14px;">@lazar181</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 Ari/Sera @ 🛌. \| Data \| Ari/Sera @ 🛌 \| \| --- \| --- \| \| Tweets downloaded \| 3241 \| \| Retweets \| 362 \| \| Short tweets \| 668 \| \| Tweets kept \| 2211 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21d2ewj0/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 @lazar181's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ukmb9ye) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ukmb9ye/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/lazar181') 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)
milyiyo/multi-minilm-finetuned-amazon-review	milyiyo	2022-01-16T22:53:05Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 - precision - recall model-index: - name: multi-minilm-finetuned-amazon-review results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi args: es metrics: - name: Accuracy type: accuracy value: 0.5422 - name: F1 type: f1 value: 0.543454465221178 - name: Precision type: precision value: 0.5452336215624385 - name: Recall type: recall value: 0.5422 --- <!-- 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-minilm-finetuned-amazon-review This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.5422 - F1: 0.5435 - Precision: 0.5452 - Recall: 0.5422 ## 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: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.0049 \| 1.0 \| 2500 \| 1.0616 \| 0.5352 \| 0.5268 \| 0.5347 \| 0.5352 \| \| 0.9172 \| 2.0 \| 5000 \| 1.0763 \| 0.5432 \| 0.5412 \| 0.5444 \| 0.5432 \| \| 0.8285 \| 3.0 \| 7500 \| 1.1077 \| 0.5408 \| 0.5428 \| 0.5494 \| 0.5408 \| \| 0.7361 \| 4.0 \| 10000 \| 1.1743 \| 0.5342 \| 0.5399 \| 0.5531 \| 0.5342 \| \| 0.6538 \| 5.0 \| 12500 \| 1.2436 \| 0.5422 \| 0.5435 \| 0.5452 \| 0.5422 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Shushant/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT	Shushant	2022-01-16T15:54:15Z	55	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer model-index: - name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT 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. --> # BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7515 ## 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 22 \| 3.9518 \| \| No log \| 2.0 \| 44 \| 3.2703 \| \| No log \| 3.0 \| 66 \| 2.9308 \| \| No log \| 4.0 \| 88 \| 2.7806 \| \| No log \| 5.0 \| 110 \| 2.6926 \| \| No log \| 6.0 \| 132 \| 2.7043 \| \| No log \| 7.0 \| 154 \| 2.7113 \| \| No log \| 8.0 \| 176 \| 2.7236 \| \| No log \| 9.0 \| 198 \| 2.7559 \| \| No log \| 10.0 \| 220 \| 2.7515 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Shushant/biobert-v1.1-biomedicalQuestionAnswering	Shushant	2022-01-16T15:34:49Z	83	5	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model-index: - name: biobert-v1.1-biomedicalQuestionAnswering 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. --> # biobert-v1.1-biomedicalQuestionAnswering This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9009 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 22 \| 3.7409 \| \| No log \| 2.0 \| 44 \| 3.1852 \| \| No log \| 3.0 \| 66 \| 3.0342 \| \| No log \| 4.0 \| 88 \| 2.9416 \| \| No log \| 5.0 \| 110 \| 2.9009 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Sakil/imdbsentdistilbertmodel	Sakil	2022-01-16T06:54:14Z	6	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "text Classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en tags: - text Classification license: apache-2.0 widget: - text: "I like you. </s></s> I love you." --- * IMDBSentimentDistilBertModel: - I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification. from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments model = DistilBertForSequenceClassification.from_pretrained('./imdbsentdistilbertmodel')
anzorq/t5-v1_1-small-ru_kbd-cased	anzorq	2022-01-16T05:24:51Z	13	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "translation", "ru", "kbd", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - ru - kbd tags: - translation datasets: - anzorq/kbd-ru-1.67M-temp - 17753 Russian-Kabardian pairs of text widget: - text: "ru->kbd: Я иду домой." example_title: "Я иду домой." - text: "ru->kbd: Дети играют во дворе." example_title: "Дети играют во дворе." - text: "ru->kbd: Сколько тебе лет?" example_title: "Сколько тебе лет?" --- ## [google/t5-v1_1-small](google/t5-v1_1-small) model ### pretrained on [anzorq/kbd-ru-1.67M-temp](https://huggingface.co/datasets/anzorq/kbd-ru-1.67M-temp) ### fine-tuned on 17753 Russian-Kabardian word/sentence pairs kbd text uses custom latin script for optimization reasons. Translation input should start with 'ru->kbd: '. Tokenizer: T5 sentencepiece, char, cased.
milyiyo/minilm-finetuned-emotion	milyiyo	2022-01-16T00:37:00Z	3	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: minilm-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: F1 type: f1 value: 0.931192 --- Based model: [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) Dataset: [emotion](https://huggingface.co/datasets/emotion) These are the results on the evaluation set: \| Attribute \| Value \| \| ------------------ \| -------- \| \| Training Loss \| 0.163100 \| \| Validation Loss \| 0.192153 \| \| F1 \| 0.931192 \|
husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1	husnu	2022-01-15T20:09:15Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1 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-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1 This model is a fine-tuned version of [husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3](https://huggingface.co/husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.4196 ## 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: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5885 \| 1.0 \| 2245 \| 1.4196 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Fraser/to_delete	Fraser	2022-01-15T15:08:51Z	0	0	null	[ "program-synthesis", "en", "dataset:program-synthesis", "license:mit", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - en thumbnail: "https://huggingface.co/Fraser/program-synthesis/resolve/main/img.png" tags: - program-synthesis license: "mit" datasets: - program-synthesis --- # Program Synthesis Data Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec). Currently just supports text & list data. ```python _FEATURES = datasets.Features( { "description": datasets.Value("string"), "input": datasets.Value("string"), "output": datasets.Value("string"), "types": datasets.Value("string") } ) ``` ![](https://huggingface.co/Fraser/program-synthesis/resolve/main/img.png)
pediberto/autonlp-testing-504313966	pediberto	2022-01-15T15:02:13Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "unk", "dataset:pediberto/autonlp-data-testing", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - pediberto/autonlp-data-testing co2_eq_emissions: 12.994518654810642 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 504313966 - CO2 Emissions (in grams): 12.994518654810642 ## Validation Metrics - Loss: 0.19673296809196472 - Accuracy: 0.9398032027783138 - Precision: 0.9133115705476967 - Recall: 0.9718255499807025 - AUC: 0.985316873222122 - F1: 0.9416604338070308 ## 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/pediberto/autonlp-testing-504313966 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("pediberto/autonlp-testing-504313966", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("pediberto/autonlp-testing-504313966", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
Ifromspace/GRIEFSOFT	Ifromspace	2022-01-15T13:06:43Z	9	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "4ulan", "ru", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: - ru tags: - PyTorch - Transformers - 4ulan --- Fork of https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2 Забавное для дискордика))00)) ROADMAP: - Собираю датасетик из книжек про попаданцев. <------------------------- Сейчас тут. - Дообучаю. - Выбрасываю в дискордик. https://discord.gg/HpeadKH
Huertas97/es_roberta_base_bne_leetspeak_ner	Huertas97	2022-01-15T11:55:46Z	4	1	spacy	[ "spacy", "token-classification", "es", "license:apache-2.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- tags: - spacy - token-classification language: - es license: apache-2.0 widget: - text: "La C0v!d es un 3ng@ño de los G0b!3rno$" example_title: "Word camouflage detection" model-index: - name: es_roberta_base_bne_leetspeak_ner results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.8979055626 - name: NER Recall type: recall value: 0.9393701406 - name: NER F Score type: f_score value: 0.9181699547 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `es_roberta_base_bne_leetspeak_ner` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model a transformer-based masked language model for the Spanish language pre-trained with a total of 570GB of clean and deduplicated text compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) <br> [LeetSpeak-NER](https://huggingface.co/spaces/Huertas97/LeetSpeak-NER) app where this model is in production for countering information disorders\| \| License \| Apache 2.0 \| \| Author \| [Álvaro Huertas García](https://www.linkedin.com/in/alvaro-huertas-garcia/) at [AI+DA](http://aida.etsisi.upm.es/) \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `INV_CAMO`, `LEETSPEAK`, `MIX`, `PUNCT_CAMO` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 91.82 \| \| `ENTS_P` \| 89.79 \| \| `ENTS_R` \| 93.94 \| \| `TRANSFORMER_LOSS` \| 166484.92 \| \| `NER_LOSS` \| 318457.35 \|
husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3	husnu	2022-01-15T07:25:53Z	25	2	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3 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-turkish-128k-cased-finetuned_lr-2e-05_epochs-3 This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the turkish squad dataset. It achieves the following results on the evaluation set: - Loss: 1.4724 ## 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: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3911 \| 1.0 \| 1281 \| 1.4900 \| \| 0.9058 \| 2.0 \| 2562 \| 1.3471 \| \| 0.6747 \| 3.0 \| 3843 \| 1.4724 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Abirate/gpt_3_finetuned_multi_x_science	Abirate	2022-01-15T06:16:57Z	28	2	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- - Text Generation - PyTorch - Transformers - gpt_neo - text generation --- ## Petrained Model Description: Open Source Version of GPT-3 Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI GPT-Neo (125M) is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model. and first released in this [repository](https://github.com/EleutherAI/gpt-neo). ## Fine-tuned Model Description: GPT-3 fine-tuned Multi-XScience The Open Source version of GPT-3: GPT-Neo(125M) has been fine-tuned on a dataset called "Multi-XScience": [Multi-XScience_Repository](https://github.com/yaolu/Multi-XScience): A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. I first fine-tuned and then deployed it using Google "Material Design" (on Anvil): [Abir Scientific text Generator](https://abir-scientific-text-generator.anvil.app/) By fine-tuning GPT-Neo(Open Source version of GPT-3), on Multi-XScience dataset, the model is now able to generate scientific texts(even better than GPT-J(6B). Try putting the prompt "attention is all" on both my [Abir Scientific text Generator](https://abir-scientific-text-generator.anvil.app/) and on the [ GPT-J Eleuther.ai Demo](https://6b.eleuther.ai/) to understand what I mean. And Here's a demonstration video for this. [Video real-time Demontration](https://www.youtube.com/watch?v=XP8uZfnCYQI)
husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6	husnu	2022-01-15T05:09:21Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6 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. --> # xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the Turkish squad dataset. It achieves the following results on the evaluation set: - Loss: 2.8135 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 350 \| 3.8389 \| \| 4.4474 \| 2.0 \| 700 \| 3.3748 \| \| 3.512 \| 3.0 \| 1050 \| 3.0657 \| \| 3.512 \| 4.0 \| 1400 \| 2.9219 \| \| 3.1526 \| 5.0 \| 1750 \| 2.8517 \| \| 2.9972 \| 6.0 \| 2100 \| 2.8135 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3	husnu	2022-01-15T03:58:24Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3 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. --> # xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the turkish squad dataset. It achieves the following results on the evaluation set: - Loss: 2.6510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.0113 \| 1.0 \| 1050 \| 2.7529 \| \| 2.838 \| 2.0 \| 2100 \| 2.6510 \| \| 2.8168 \| 3.0 \| 3150 \| 2.6510 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/autosport-formulaoneworld-speedcafe	huggingtweets	2022-01-15T03:24:30Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/autosport-formulaoneworld-speedcafe/1642217065882/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('https://pbs.twimg.com/profile_images/1192531689060200448/S9KoiehJ_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1294927107605356544/CVXTlp9y_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468895545007775746/NIWzzmye_400x400.jpg')"> </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">Speedcafe.com & Formula One World & Autosport</div> <div style="text-align: center; font-size: 14px;">@autosport-formulaoneworld-speedcafe</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 Speedcafe.com & Formula One World & Autosport. \| Data \| Speedcafe.com \| Formula One World \| Autosport \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3250 \| 3247 \| 3250 \| \| Retweets \| 0 \| 2778 \| 52 \| \| Short tweets \| 3 \| 178 \| 15 \| \| Tweets kept \| 3247 \| 291 \| 3183 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kcn72bl0/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 @autosport-formulaoneworld-speedcafe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fq703qs) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fq703qs/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/autosport-formulaoneworld-speedcafe') 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)
NbAiLab/roberta_NCC_des_128_decayfrom200	NbAiLab	2022-01-15T00:11:52Z	4	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
abdelkader/pegasus-samsum	abdelkader	2022-01-14T21:30:36Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6936 \| 0.54 \| 500 \| 1.4844 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
socrates/socrates2.0	socrates	2022-01-14T16:07:04Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	The unexamined life is not worth living
erwanlc/t5-coktails_recipe-small	erwanlc	2022-01-14T14:32:10Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-coktails_recipe-small 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. --> # t5-coktails_recipe-small This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - 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 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
vachonni/wav2vec2-large-xls-r-300m-da-colab	vachonni	2022-01-14T12:14:53Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-da-colab 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. --> # wav2vec2-large-xls-r-300m-da-colab This model is a fine-tuned version of [Alvenir/wav2vec2-base-da](https://huggingface.co/Alvenir/wav2vec2-base-da) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
lewtun/distilbert-base-uncased-finetuned-emotion-test-01	lewtun	2022-01-14T10:29:26Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion-test-01 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.39 - name: F1 type: f1 value: 0.21884892086330932 --- <!-- 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-emotion-test-01 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: 1.7510 - Accuracy: 0.39 - F1: 0.2188 ## 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: 64 - eval_batch_size: 64 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 2 \| 1.7634 \| 0.39 \| 0.2188 \| \| No log \| 2.0 \| 4 \| 1.7510 \| 0.39 \| 0.2188 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
anirudh21/xlnet-base-cased-finetuned-rte	anirudh21	2022-01-14T07:04:23Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlnet", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: xlnet-base-cased-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6895306859205776 --- <!-- 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. --> # xlnet-base-cased-finetuned-rte This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0656 - Accuracy: 0.6895 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 156 \| 0.7007 \| 0.4874 \| \| No log \| 2.0 \| 312 \| 0.6289 \| 0.6751 \| \| No log \| 3.0 \| 468 \| 0.7020 \| 0.6606 \| \| 0.6146 \| 4.0 \| 624 \| 1.0573 \| 0.6570 \| \| 0.6146 \| 5.0 \| 780 \| 1.0656 \| 0.6895 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
zhichao158/wav2vec2-xls-r-common_voice-tr-ft	zhichao158	2022-01-14T07:03:32Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-common_voice-tr-ft 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. --> # wav2vec2-xls-r-common_voice-tr-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3736 - Wer: 0.2930 - Cer: 0.0708 ## 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: 0.0005 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 0.5462 \| 13.51 \| 500 \| 0.4423 \| 0.4807 \| 0.1188 \| \| 0.342 \| 27.03 \| 1000 \| 0.3781 \| 0.3954 \| 0.0967 \| \| 0.2272 \| 40.54 \| 1500 \| 0.3816 \| 0.3595 \| 0.0893 \| \| 0.1805 \| 54.05 \| 2000 \| 0.3943 \| 0.3487 \| 0.0854 \| \| 0.1318 \| 67.57 \| 2500 \| 0.3818 \| 0.3262 \| 0.0801 \| \| 0.1213 \| 81.08 \| 3000 \| 0.3777 \| 0.3113 \| 0.0758 \| \| 0.0639 \| 94.59 \| 3500 \| 0.3788 \| 0.2953 \| 0.0716 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.8.0 - Datasets 1.17.0 - Tokenizers 0.10.3
LACAI/DialoGPT-small-PFG	LACAI	2022-01-14T01:36:36Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019) Three additional special tokens were added during the fine-tuning process: - <\|pad\|> padding token - <\|user\|> speaker control token to prompt user responses - <\|system\|> speaker control token to prompt system responses The following Dialogues were excluded: - Those with donation amounts outside of the task range of [$0, $2]. - Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue. - Those with more than 800 words. Stats: - Training set: 519 dialogues - Validation set: 58 dialogues - ~20 utterances per dialogue
bob80333/speechbrain_ja2en_st_63M_yt600h	bob80333	2022-01-14T00:45:47Z	18	1	speechbrain	[ "speechbrain", "speech-translation", "CTC", "Attention", "Transformer", "pytorch", "automatic-speech-recognition", "en", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: "en" thumbnail: tags: - speech-translation - CTC - Attention - Transformer - pytorch - speechbrain - automatic-speech-recognition metrics: - BLEU --- # Conformer Encoder/Decoder for Speech Translation This model was trained with [SpeechBrain](https://speechbrain.github.io), and is based on the Fisher Callhome recipie. The performance of the model is the following: \| Release \| CoVoSTv2 JA->EN Test BLEU \| Custom Dataset Validation BLEU \| Custom Dataset Test BLEU \| GPUs \| \|:-------------:\|:--------------:\|:--------------:\|:--------------:\|:--------:\| \| 01-13-21 \| 9.73 \| 8.38 \| 12.01 \| 1xRTX 3090 \| This model was trained on subtitled audio downloaded from YouTube, and was not fine-tuned on the CoVoSTv2 training set. When calculating the BLEU score for CoVoSTv2, the utterances were first preprocessed by the same pipeline that preprocessed the original data for the model, which includes removing all punctuation outside of apostrophes, and removing capitalization, similar to the data preprocessing done for the Fisher Callhome dataset in the speechbrain recipe. ## Pipeline description The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed. ## Install SpeechBrain First of all, install SpeechBrain with the following command: ``` pip install speechbrain ``` ### Transcribing your own audio files (Spoken Japanese, to written English) ```python from speechbrain.pretrained import EncoderDecoderASR st_model = EncoderDecoderASR.from_hparams(source="bob80333/speechbrain_ja2en_st_63M_yt600h") st_model.transcribe_file("your_file_here.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Limitations: The model is likely to get caught in repetitions. The model is not very good at translation, which is reflected by its low BLEU scores. The outputs of this model are unlikely to be correct, do not rely on it for any serious purpose. This model was trained on data from Youtube, and has inherited whatever biases can be found in Youtube audio/subtitles. The creator of this model doesn't actually know Japanese.
atu/paddle_detection	atu	2022-01-14T00:33:03Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	一个测试Paddle 服务器模型的项目
Suzana/new-york-tokyo-london	Suzana	2022-01-13T17:53:58Z	70	5	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: new-york-tokyo-london results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9104477763175964 --- # new-york-tokyo-london Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### London ![London](images/London.jpg) #### New York ![New York](images/New_York.jpg) #### Tokyo ![Tokyo](images/Tokyo.jpg)
alaggung/bart-rl	alaggung	2022-01-13T17:18:17Z	180	0	transformers	[ "transformers", "pytorch", "tf", "bart", "text2text-generation", "summarization", "ko", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: - ko tags: - summarization widget: - text: "[BOS]밥 ㄱ?[SEP]고고고고 뭐 먹을까?[SEP]어제 김치찌개 먹어서 한식말고 딴 거[SEP]그럼 돈까스 어때?[SEP]오 좋다 1시 학관 앞으로 오셈[SEP]ㅇㅋ[EOS]" inference: parameters: max_length: 64 top_k: 5 --- # BART R3F [2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다. [bart-r3f](https://huggingface.co/alaggung/bart-r3f) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 RL 기법을 적용해 대화요약 Task를 학습한 모델입니다. 데이터는 [AIHub 한국어 대화요약](https://aihub.or.kr/aidata/30714) 데이터를 사용하였습니다.
keras-io/graph-attention-nets	keras-io	2022-01-13T14:54:10Z	8	6	tf-keras	[ "tf-keras", "graph neural networks", "arxiv:1710.10903", "license:cc0-1.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- thumbnail: "url to a thumbnail used in social sharing" tags: - graph neural networks license: - cc0-1.0 --- ## Keras Implementation of Graph Attention Networks for Node Classification 🕸 This repo contains the model and the notebook [to this Keras example on Graph Attention Networks for Node Classification](https://keras.io/examples/graph/gat_node_classification/). Full credits to: [Alexander Kensert](https://github.com/akensert) ## Background Information Graph neural networks is the preferred neural network architecture for processing data structured as graphs (for example, social networks or molecule structures), yielding better results than fully-connected networks or convolutional networks. This tutorial implements a specific graph neural network known as a [Graph Attention Network (GAT)](https://arxiv.org/abs/1710.10903) to predict labels of scientific papers based on the papers they cite (using the [Cora dataset](https://linqs.soe.ucsc.edu/data)). References For more information on GAT, see the original paper [Graph Attention Networks](https://arxiv.org/abs/1710.10903) as well as [DGL's Graph Attention Networks](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/9_gat.html) documentation.
keras-io/time-series-anomaly-detection-autoencoder	keras-io	2022-01-13T14:52:51Z	14	13	tf-keras	[ "tf-keras", "autoencoder", "time series", "anomaly detection", "license:cc0-1.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- tags: - autoencoder - time series - anomaly detection license: - cc0-1.0 --- ## Keras Implementation of time series anomaly detection using an Autoencoder ⌛ This repo contains the model and the notebook [for this time series anomaly detection implementation of Keras](https://keras.io/examples/timeseries/timeseries_anomaly_detection/). Full credits to: [Pavithra Vijay](https://github.com/pavithrasv) ## Background Information This notebook demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data.
keras-io/simple-mnist-convnet	keras-io	2022-01-13T14:52:44Z	2	0	tf-keras	[ "tf-keras", "lstm", "license:cc0-1.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- tags: - lstm license: - cc0-1.0 --- ## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣ This repo contains the model and the notebook [on Simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/). Full credits to: [François Chollet](https://github.com/fchollet)
keras-io/image-captioning	keras-io	2022-01-13T14:52:23Z	0	8	generic	[ "generic", "tf-keras", "image-to-text", "arxiv:1502.03044", "license:cc0-1.0", "region:us" ]	image-to-text	2022-03-02T23:29:05Z	--- tags: - image-to-text - generic library_name: generic pipeline_tag: image-to-text widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg example_title: Kedis - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg example_title: Cat in a Crate - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-3.jpg example_title: Two Cats Chilling license: cc0-1.0 --- ## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. 🌃🌅🎑 This repo contains the models and the notebook [on Image captioning with visual attention](https://www.tensorflow.org/tutorials/text/image_captioning?hl=en). Full credits to TensorFlow Team ## Background Information This notebook implements TensorFlow Keras implementation on Image captioning with visual attention. Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave". ![image](https://www.tensorflow.org/images/surf.jpg) To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption. ![attention](https://www.tensorflow.org/images/imcap_prediction.png) The model architecture is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044). This notebook is an end-to-end example. When you run the notebook, it downloads the [MS-COCO](https://cocodataset.org/#home) dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model.
espnet/vectominist_seame_asr_conformer_bpe5626	espnet	2022-01-13T14:49:52Z	1	1	espnet	[ "espnet", "audio", "automatic-speech-recognition", "en", "zh", "multilingual", "dataset:seame", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - espnet - audio - automatic-speech-recognition language: - en - zh - multilingual datasets: - seame license: cc-by-4.0 --- ## ESPnet2 ASR pretrained model ### `https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1` ♻️ Imported from https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1 This model was trained by vectominist using seame/asr1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
nielsr/tapex-large-finetuned-sqa	nielsr	2022-01-13T14:41:16Z	7	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:msr_sqa", "arxiv:2107.07653", "license:apache-2.0", "autotrain_compatible", "region:us" ]	table-question-answering	2022-03-02T23:29:05Z	--- language: en tags: - tapex - table-question-answering license: apache-2.0 datasets: - msr_sqa inference: false --- TAPEX-large model fine-tuned on SQA. This model 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. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining). To load it and run inference, you can do the following: ``` from transformers import BartTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-sqa") model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large-finetuned-sqa") # create table data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} table = pd.DataFrame.from_dict(data) # turn into dict table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]} # turn into format TAPEX expects # define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) # add question question = "how many movies does George Clooney have?" joint_input = question + " " + linear_table # encode encoding = tokenizer(joint_input, return_tensors="pt") # forward pass outputs = model.generate(**encoding) # decode tokenizer.batch_decode(outputs, skip_special_tokens=True) ```
anirudh21/xlnet-base-cased-finetuned-wnli	anirudh21	2022-01-13T13:52:38Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlnet", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: xlnet-base-cased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 --- <!-- 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. --> # xlnet-base-cased-finetuned-wnli This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6874 - Accuracy: 0.5634 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.7209 \| 0.5352 \| \| No log \| 2.0 \| 80 \| 0.6874 \| 0.5634 \| \| No log \| 3.0 \| 120 \| 0.6908 \| 0.5634 \| \| No log \| 4.0 \| 160 \| 0.6987 \| 0.4930 \| \| No log \| 5.0 \| 200 \| 0.6952 \| 0.5634 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
mahaamami/distilroberta-base-model-transcript	mahaamami	2022-01-13T13:28:24Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-model-transcript 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. --> # distilroberta-base-model-transcript This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8922 ## 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: 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.1193 \| 1.0 \| 5570 \| 1.9873 \| \| 2.0502 \| 2.0 \| 11140 \| 1.9304 \| \| 1.9718 \| 3.0 \| 16710 \| 1.8922 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
huggingtweets/tsuda	huggingtweets	2022-01-13T08:46:49Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/tsuda/1642063525628/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('https://pbs.twimg.com/profile_images/1433345543963508738/qEUwKlFD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">津田大介</div> <div style="text-align: center; font-size: 14px;">@tsuda</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 津田大介. \| Data \| 津田大介 \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 2873 \| \| Short tweets \| 227 \| \| Tweets kept \| 144 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/o0sc3rb4/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 @tsuda's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qjnl0op) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qjnl0op/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/tsuda') 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/h_ototake-hirox246-ochyai	huggingtweets	2022-01-13T07:45:50Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/h_ototake-hirox246-ochyai/1642059945521/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('https://pbs.twimg.com/profile_images/646595746905620480/oeKI14gB_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1072419376668782597/hhmhNVER_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1481142443068198912/NCrXoLUB_400x400.jpg')"> </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">ひろゆき, Hiroyuki Nishimura & 落合陽一 Yoichi OCHIAI & 乙武洋匡</div> <div style="text-align: center; font-size: 14px;">@h_ototake-hirox246-ochyai</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 ひろゆき, Hiroyuki Nishimura & 落合陽一 Yoichi OCHIAI & 乙武洋匡. \| Data \| ひろゆき, Hiroyuki Nishimura \| 落合陽一 Yoichi OCHIAI \| 乙武洋匡 \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3248 \| 3240 \| 3238 \| \| Retweets \| 281 \| 2238 \| 1259 \| \| Short tweets \| 1980 \| 574 \| 1437 \| \| Tweets kept \| 987 \| 428 \| 542 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3k39l31f/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 @h_ototake-hirox246-ochyai's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1d9okxed) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1d9okxed/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/h_ototake-hirox246-ochyai') 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)
ju-bezdek/slovakbert-conll2003-sk-ner	ju-bezdek	2022-01-12T20:37:34Z	9	1	transformers	[ "transformers", "pytorch", "generated_from_trainer", "dataset:ju-bezdek/conll2003-SK-NER", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - ju-bezdek/conll2003-SK-NER metrics: - precision - recall - f1 - accuracy model-index: - name: outputs results: - task: name: Token Classification type: token-classification dataset: name: ju-bezdek/conll2003-SK-NER type: ju-bezdek/conll2003-SK-NER args: conll2003-SK-NER metrics: - name: Precision type: precision value: 0.8189727994593682 - name: Recall type: recall value: 0.8389581169955002 - name: F1 type: f1 value: 0.8288450029922203 - name: Accuracy type: accuracy value: 0.9526157920337243 --- <!-- 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. --> # outputs This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset. It achieves the following results on the evaluation (validation) set: - Loss: 0.1752 - Precision: 0.8190 - Recall: 0.8390 - F1: 0.8288 - Accuracy: 0.9526 ## Model description More information needed ## Code example ```python: from transformers import pipeline, AutoModel, AutoTokenizer from spacy import displacy import os model_path="ju-bezdek/slovakbert-conll2003-sk-ner" aggregation_strategy="max" ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy) input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax" ner_ents = ner_pipeline(input_sentence) print(ner_ents) ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0] options = {"ents":ent_group_labels} dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents] displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True) ``` ### Result: <div> <span class="tex2jax_ignore"><div class="entities" style="line-height: 2.5; direction: ltr"> <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Ruský <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">MISC</span> </mark> premiér <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Viktor Černomyrdin <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> v piatok povedal, že prezident <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Boris Jeľcin, <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> , ktorý je na dovolenke mimo <mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Moskvy <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> </mark> , podporil mierový plán šéfa bezpečnosti <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Alexandra Lebedu <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> pre <mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Čečensko, <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> </mark> uviedla tlačová agentúra <mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Interfax <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORG</span> </mark> </div></span> </div> ## 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: 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.3237 \| 1.0 \| 878 \| 0.2541 \| 0.7125 \| 0.8059 \| 0.7563 \| 0.9283 \| \| 0.1663 \| 2.0 \| 1756 \| 0.2370 \| 0.7775 \| 0.8090 \| 0.7929 \| 0.9394 \| \| 0.1251 \| 3.0 \| 2634 \| 0.2289 \| 0.7732 \| 0.8029 \| 0.7878 \| 0.9385 \| \| 0.0984 \| 4.0 \| 3512 \| 0.2818 \| 0.7294 \| 0.8189 \| 0.7715 \| 0.9294 \| \| 0.0808 \| 5.0 \| 4390 \| 0.3138 \| 0.7615 \| 0.7900 \| 0.7755 \| 0.9326 \| \| 0.0578 \| 6.0 \| 5268 \| 0.3072 \| 0.7548 \| 0.8222 \| 0.7871 \| 0.9370 \| \| 0.0481 \| 7.0 \| 6146 \| 0.2778 \| 0.7897 \| 0.8156 \| 0.8025 \| 0.9408 \| \| 0.0414 \| 8.0 \| 7024 \| 0.3336 \| 0.7695 \| 0.8201 \| 0.7940 \| 0.9389 \| \| 0.0268 \| 9.0 \| 7902 \| 0.3294 \| 0.7868 \| 0.8140 \| 0.8002 \| 0.9409 \| \| 0.0204 \| 10.0 \| 8780 \| 0.3693 \| 0.7657 \| 0.8239 \| 0.7938 \| 0.9376 \| \| 0.016 \| 11.0 \| 9658 \| 0.3816 \| 0.7932 \| 0.8242 \| 0.8084 \| 0.9425 \| \| 0.0108 \| 12.0 \| 10536 \| 0.3607 \| 0.7929 \| 0.8256 \| 0.8089 \| 0.9431 \| \| 0.0078 \| 13.0 \| 11414 \| 0.3980 \| 0.7915 \| 0.8240 \| 0.8074 \| 0.9423 \| \| 0.0062 \| 14.0 \| 12292 \| 0.4096 \| 0.7995 \| 0.8247 \| 0.8119 \| 0.9436 \| \| 0.0035 \| 15.0 \| 13170 \| 0.4177 \| 0.8006 \| 0.8251 \| 0.8127 \| 0.9438 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
vinaydngowda/Robertabase_Ana4	vinaydngowda	2022-01-12T20:12:16Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "en", "dataset:vinaydngowda/autonlp-data-case-classify-xlnet", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - vinaydngowda/autonlp-data-case-classify-xlnet co2_eq_emissions: 19.964760910364927 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 496213536 - CO2 Emissions (in grams): 19.964760910364927 ## Validation Metrics - Loss: 0.7149562835693359 - Accuracy: 0.8092592592592592 - Macro F1: 0.8085189591849891 - Micro F1: 0.8092592592592593 - Weighted F1: 0.8085189591849888 - Macro Precision: 0.8137745564384112 - Micro Precision: 0.8092592592592592 - Weighted Precision: 0.8137745564384112 - Macro Recall: 0.8092592592592592 - Micro Recall: 0.8092592592592592 - Weighted Recall: 0.8092592592592592 ## 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/vinaydngowda/autonlp-case-classify-xlnet-496213536 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
Katiejdarby/test1	Katiejdarby	2022-01-12T18:36:31Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:04Z	this is a test. How do you write a paper?
Jainil30/wav2vec2-base-csa-10-rev3	Jainil30	2022-01-12T14:55:33Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-csa-10-rev3 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. --> # wav2vec2-base-csa-10-rev3 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5869 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 18.7934 \| 25.0 \| 200 \| 3.5869 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
hogger32/xlmRoberta-for-VietnameseQA	hogger32	2022-01-12T14:43:00Z	27	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: xlmRoberta-for-VietnameseQA 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. --> # xlmRoberta-for-VietnameseQA This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.8315 ## Model description Fine-tuned by Honganh Nguyen (FPTU AI Club). ## Intended uses & limitations More information needed ## Training and evaluation data Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data. Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5701 \| 1.0 \| 2534 \| 1.2220 \| \| 1.2942 \| 2.0 \| 5068 \| 0.9698 \| \| 1.0693 \| 3.0 \| 7602 \| 0.8315 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
mahaamami/distilroberta-base-finetuned-wikitext2	mahaamami	2022-01-12T13:25:49Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8833 ## 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: 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.1026 \| 1.0 \| 5835 \| 1.9705 \| \| 2.0088 \| 2.0 \| 11670 \| 1.9090 \| \| 1.9766 \| 3.0 \| 17505 \| 1.8833 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
justin871030/bert-base-uncased-goemotions-ekman-finetuned	justin871030	2022-01-12T12:44:49Z	7	0	transformers	[ "transformers", "pytorch", "bert", "go-emotion", "text-classification", "en", "dataset:go_emotions", "license:mit", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: en tags: - go-emotion - text-classification - pytorch datasets: - go_emotions metrics: - f1 widget: - text: "Thanks for giving advice to the people who need it! 👌🙏" license: mit --- ## Model Description 1. Based on the uncased BERT pretrained model with a linear output layer. 2. Added several commonly-used emoji and tokens to the special token list of the tokenizer. 3. Did label smoothing while training. 4. Used weighted loss and focal loss to help the cases which trained badly.
anirudh21/distilbert-base-uncased-finetuned-qnli	anirudh21	2022-01-12T12:39:07Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-qnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6064981949458483 --- <!-- 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-qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8121 - Accuracy: 0.6065 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 156 \| 0.6949 \| 0.4874 \| \| No log \| 2.0 \| 312 \| 0.6596 \| 0.5957 \| \| No log \| 3.0 \| 468 \| 0.7186 \| 0.5812 \| \| 0.6026 \| 4.0 \| 624 \| 0.7727 \| 0.6029 \| \| 0.6026 \| 5.0 \| 780 \| 0.8121 \| 0.6065 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
ibraheemmoosa/xlmindic-base-uniscript-soham	ibraheemmoosa	2022-01-12T12:28:05Z	4	0	transformers	[ "transformers", "pytorch", "tf", "jax", "albert", "text-classification", "multilingual", "xlmindic", "nlp", "indoaryan", "indicnlp", "iso15919", "transliteration", "as", "bn", "gu", "hi", "mr", "ne", "or", "pa", "si", "sa", "bpy", "mai", "bh", "gom", "dataset:oscar", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - as - bn - gu - hi - mr - ne - or - pa - si - sa - bpy - mai - bh - gom license: apache-2.0 datasets: - oscar tags: - multilingual - albert - xlmindic - nlp - indoaryan - indicnlp - iso15919 - transliteration - text-classification widget: - text : 'cīnēra madhyāñcalē āraō ēkaṭi śaharēra bāsindārā ābāra gharabandī haẏē paṛēchēna. āja maṅgalabāra natuna karē lakaḍāuna–saṁkrānta bidhiniṣēdha jāri haōẏāra para gharē āṭakā paṛēchēna tām̐rā. karōnāra ati saṁkrāmaka natuna dharana amikranēra bistāra ṭhēkātē ēmana padakṣēpa niẏēchē kartr̥pakṣa. khabara bārtā saṁsthā ēēphapira.' co2_eq_emissions: emissions: "0.21 in grams of CO2" source: "calculated using this webstie https://mlco2.github.io/impact/#compute" training_type: "fine-tuning" geographical_location: "NA" hardware_used: "P100 for about 1.5 hours" --- # XLMIndic Base Uniscript This model is finetuned from [this model](https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript) on Soham Bangla News Classification task which is part of the IndicGLUE benchmark. Before pretraining this model we transliterate the text to [ISO-15919](https://en.wikipedia.org/wiki/ISO_15919) format using the [Aksharamukha](https://pypi.org/project/aksharamukha/) library. A demo of Aksharamukha library is hosted [here](https://aksharamukha.appspot.com/converter) where you can transliterate your text and use it on our model on the inference widget. ## Model description This model has the same configuration as the [ALBERT Base v2 model](https://huggingface.co/albert-base-v2/). Specifically, this model has the following configuration: - 12 repeating layers - 128 embedding dimension - 768 hidden dimension - 12 attention heads - 11M parameters - 512 sequence length ## Training data This model was fine-tuned on Soham dataset that is part of the IndicGLUE benchmark. ## Transliteration The unique component of this model is that it takes in ISO-15919 transliterated text. The motivation behind this is this. When two languages share vocabularies, a machine learning model can exploit that to learn good cross-lingual representations. However if these two languages use different writing scripts it is difficult for a model to make the connection. Thus if if we can write the two languages in a single script then it is easier for the model to learn good cross-lingual representation. For many of the scripts currently in use, there are standard transliteration schemes to convert to the Latin script. In particular, for the Indic scripts the ISO-15919 transliteration scheme is designed to consistently transliterate texts written in different Indic scripts to the Latin script. An example of ISO-15919 transliteration for a piece of Bangla text is the following: Original: "রবীন্দ্রনাথ ঠাকুর এফআরএএস (৭ মে ১৮৬১ - ৭ আগস্ট ১৯৪১; ২৫ বৈশাখ ১২৬৮ - ২২ শ্রাবণ ১৩৪৮ বঙ্গাব্দ) ছিলেন অগ্রণী বাঙালি কবি, ঔপন্যাসিক, সংগীতস্রষ্টা, নাট্যকার, চিত্রকর, ছোটগল্পকার, প্রাবন্ধিক, অভিনেতা, কণ্ঠশিল্পী ও দার্শনিক।" Transliterated: 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kabi, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika.' Another example for a piece of Hindi text is the following: Original: "चूंकि मानव परिवार के सभी सदस्यों के जन्मजात गौरव और समान तथा अविच्छिन्न अधिकार की स्वीकृति ही विश्व-शान्ति, न्याय और स्वतन्त्रता की बुनियाद है" Transliterated: "cūṁki mānava parivāra kē sabhī sadasyōṁ kē janmajāta gaurava aura samāna tathā avicchinna adhikāra kī svīkr̥ti hī viśva-śānti, nyāya aura svatantratā kī buniyāda hai" ## Training procedure ### Preprocessing The texts are transliterated to ISO-15919 format using the Aksharamukha library. Then these are tokenized using SentencePiece and a vocabulary size of 50,000. ### Training The model was trained for 8 epochs with a batch size of 16 and a learning rate of 2e-5. ## Evaluation results See results specific to Soham in the following table. ### IndicGLUE Task \| mBERT \| XLM-R \| IndicBERT-Base \| XLMIndic-Base-Uniscript (This Model) \| XLMIndic-Base-Multiscript (Ablation Model) -----\| ----- \| ----- \| ------ \| ------- \| -------- Wikipedia Section Title Prediction \| 71.90 \| 65.45 \| 69.40 \| 81.78 ± 0.60 \| 77.17 ± 0.76 Article Genre Classification \| 88.64 \| 96.61 \| 97.72 \| 98.70 ± 0.29 \| 98.30 ± 0.26 Named Entity Recognition (F1-score) \| 71.29 \| 62.18 \| 56.69 \| 89.85 ± 1.14 \| 83.19 ± 1.58 BBC Hindi News Article Classification \| 60.55 \| 75.52 \| 74.60 \| 79.14 ± 0.60 \| 77.28 ± 1.50 Soham Bangla News Article Classification \| 80.23 \| 87.6 \| 78.45 \| 93.89 ± 0.48 \| 93.22 ± 0.49 INLTK Gujarati Headlines Genre Classification \| - \| - \| 92.91 \| 90.73 ± 0.75 \| 90.41 ± 0.69 INLTK Marathi Headlines Genre Classification \| - \| - \| 94.30 \| 92.04 ± 0.47 \| 92.21 ± 0.23 IITP Hindi Product Reviews Sentiment Classification \| 74.57 \| 78.97 \| 71.32 \| 77.18 ± 0.77 \| 76.33 ± 0.84 IITP Hindi Movie Reviews Sentiment Classification \| 56.77 \| 61.61 \| 59.03 \| 66.34 ± 0.16 \| 65.91 ± 2.20 MIDAS Hindi Discourse Type Classification \| 71.20 \| 79.94 \| 78.44 \| 78.54 ± 0.91 \| 78.39 ± 0.33 Cloze Style Question Answering (Fill-mask task) \| - \| - \| 37.16 \| 41.54 \| 38.21 ## Intended uses & limitations This model is pretrained on Indo-Aryan languages. Thus it is intended to be used for downstream tasks on these languages. However, since Dravidian languages such as Malayalam, Telegu, Kannada etc share a lot of vocabulary with the Indo-Aryan languages, this model can potentially be used on those languages too (after transliterating the text to ISO-15919). You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=xlmindic) 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 To use this model you will need to first install the [Aksharamukha](https://pypi.org/project/aksharamukha/) library. ```bash pip install aksharamukha ``` Using this library you can transliterate any text wriiten in Indic scripts in the following way: ```python >>> from aksharamukha import transliterate >>> text = "चूंकि मानव परिवार के सभी सदस्यों के जन्मजात गौरव और समान तथा अविच्छिन्न अधिकार की स्वीकृति ही विश्व-शान्ति, न्याय और स्वतन्त्रता की बुनियाद है" >>> transliterated_text = transliterate.process('autodetect', 'ISO', text) >>> transliterated_text "cūṁki mānava parivāra kē sabhī sadasyōṁ kē janmajāta gaurava aura samāna tathā avicchinna adhikāra kī svīkr̥ti hī viśva-śānti, nyāya aura svatantratā kī buniyāda hai" ``` Then you can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> from aksharamukha import transliterate >>> unmasker = pipeline('fill-mask', model='ibraheemmoosa/xlmindic-base-uniscript') >>> text = "রবীন্দ্রনাথ ঠাকুর এফআরএএস (৭ মে ১৮৬১ - ৭ আগস্ট ১৯৪১; ২৫ বৈশাখ ১২৬৮ - ২২ শ্রাবণ ১৩৪৮ বঙ্গাব্দ) ছিলেন অগ্রণী বাঙালি [MASK], ঔপন্যাসিক, সংগীতস্রষ্টা, নাট্যকার, চিত্রকর, ছোটগল্পকার, প্রাবন্ধিক, অভিনেতা, কণ্ঠশিল্পী ও দার্শনিক। ১৯১৩ সালে গীতাঞ্জলি কাব্যগ্রন্থের ইংরেজি অনুবাদের জন্য তিনি এশীয়দের মধ্যে সাহিত্যে প্রথম নোবেল পুরস্কার লাভ করেন।" >>> transliterated_text = transliterate.process('Bengali', 'ISO', text) >>> transliterated_text 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli [MASK], aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama [MASK] puraskāra lābha karēna.' >>> unmasker(transliterated_text) [{'score': 0.39705055952072144, 'token': 1500, 'token_str': 'abhinētā', 'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli abhinētā, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}, {'score': 0.20499080419540405, 'token': 3585, 'token_str': 'kabi', 'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kabi, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}, {'score': 0.1314290314912796, 'token': 15402, 'token_str': 'rājanētā', 'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli rājanētā, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}, {'score': 0.060830358415842056, 'token': 3212, 'token_str': 'kalākāra', 'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli kalākāra, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}, {'score': 0.035522934049367905, 'token': 11586, 'token_str': 'sāhityakāra', 'sequence': 'rabīndranātha ṭhākura ēphaāraēēsa (7 mē 1861 - 7 āgasṭa 1941; 25 baiśākha 1268 - 22 śrābaṇa 1348 baṅgābda) chilēna agraṇī bāṅāli sāhityakāra, aupanyāsika, saṁgītasraṣṭā, nāṭyakāra, citrakara, chōṭagalpakāra, prābandhika, abhinētā, kaṇṭhaśilpī ō dārśanika. 1913 sālē gītāñjali kābyagranthēra iṁrēji anubādēra janya tini ēśīẏadēra madhyē sāhityē prathama nōbēla puraskāra lābha karēna.'}] ``` ### Limitations and bias Even though we pretrain on a comparatively large multilingual corpus the model may exhibit harmful gender, ethnic and political bias. If you fine-tune this model on a task where these issues are important you should take special care when relying on the model to make decisions. ## Contact Feel free to contact us if you have any ideas or if you want to know more about our models. - Ibraheem Muhammad Moosa ([email protected]) - Mahmud Elahi Akhter ([email protected]) - Ashfia Binte Habib ## BibTeX entry and citation info Coming soon!
GusNicho/roberta-base-finetuned	GusNicho	2022-01-12T08:31:17Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-finetuned 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. --> # roberta-base-finetuned This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.4057 - eval_runtime: 3.7087 - eval_samples_per_second: 167.712 - eval_steps_per_second: 2.696 - epoch: 2.11 - step: 2053 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
ncduy/opus-mt-en-vi-full-finetuned-en-to-vi	ncduy	2022-01-12T07:10:14Z	8	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-en-vi-full-finetuned-en-to-vi 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. --> # opus-mt-en-vi-full-finetuned-en-to-vi This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 212 - eval_batch_size: 212 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.17.0 - Tokenizers 0.10.3
Jinhwan/krelectra-base-mecab	Jinhwan	2022-01-12T03:18:55Z	5	0	transformers	[ "transformers", "pytorch", "electra", "pretraining", "korean", "ko", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: ko license: apache-2.0 tags: - korean --- # KrELECTRA-base-mecab Korean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer) ## Usage ### Load model and tokenizer ```python >>> from transformers import AutoTokenizer, AutoModelForPreTraining >>> model = AutoModelForPreTraining.from_pretrained("Jinhwan/krelectra-base-mecab") >>> tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab") ``` ### Tokenizer example ```python >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab") >>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]") ['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]'] >>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]']) [2, 7214, 24023, 24663, 26580, 3195, 7086, 3746, 5500, 17, 3]
ThePixOne/retBERT	ThePixOne	2022-01-11T18:24:24Z	8	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	BERT finetuned on wallstreetbets subreddit
avichr/heBERT_NER	avichr	2022-01-11T17:00:46Z	4,122	5	transformers	[ "transformers", "pytorch", "bert", "token-classification", "arxiv:1810.04805", "arxiv:2102.01909", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	# HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HeBERT is a Hebrew pretrained language model. It is based on [Google's BERT](https://arxiv.org/abs/1810.04805) architecture and it is BERT-Base config. <br> HeBert was trained on three dataset: 1. A Hebrew version of [OSCAR](https://oscar-corpus.com/): ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences. 2. A Hebrew dump of [Wikipedia](https://dumps.wikimedia.org/): ~650 MB of data, including over 63 millions words and 3.8 millions sentences 3. Emotion User Generated Content (UGC) data that was collected for the purpose of this study (described below). ## Named-entity recognition (NER) The ability of the model to classify named entities in text, such as persons' names, organizations, and locations; tested on a labeled dataset from [Ben Mordecai and M Elhadad (2005)](https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/), and evaluated with F1-score. ### How to use ``` from transformers import pipeline # how to use? NER = pipeline( "token-classification", model="avichr/heBERT_NER", tokenizer="avichr/heBERT_NER", ) NER('דויד לומד באוניברסיטה העברית שבירושלים') ``` ## Other tasks [Emotion Recognition Model](https://huggingface.co/avichr/hebEMO_trust). An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) <br> [Sentiment Analysis](https://huggingface.co/avichr/heBERT_sentiment_analysis). <br> [masked-LM model](https://huggingface.co/avichr/heBERT) (can be fine-tunned to any down-stream task). ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={arXiv preprint arXiv:2102.01909}, year={2021} } ``` [git](https://github.com/avichaychriqui/HeBERT)
asakawa/wav2vec2-base-demo-colab	asakawa	2022-01-11T16:22:43Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-demo-colab 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. --> # wav2vec2-base-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4500 - Wer: 0.3391 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.5329 \| 4.0 \| 500 \| 1.5741 \| 1.0400 \| \| 0.6432 \| 8.0 \| 1000 \| 0.4571 \| 0.4418 \| \| 0.2214 \| 12.0 \| 1500 \| 0.4381 \| 0.3823 \| \| 0.1294 \| 16.0 \| 2000 \| 0.4706 \| 0.3911 \| \| 0.0868 \| 20.0 \| 2500 \| 0.5252 \| 0.3662 \| \| 0.0616 \| 24.0 \| 3000 \| 0.4828 \| 0.3458 \| \| 0.0461 \| 28.0 \| 3500 \| 0.4500 \| 0.3391 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
alaggung/bart-r3f	alaggung	2022-01-11T16:18:32Z	123	6	transformers	[ "transformers", "pytorch", "tf", "bart", "text2text-generation", "summarization", "ko", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: - ko tags: - summarization widget: - text: "[BOS]밥 ㄱ?[SEP]고고고고 뭐 먹을까?[SEP]어제 김치찌개 먹어서 한식말고 딴 거[SEP]그럼 돈까스 어때?[SEP]오 좋다 1시 학관 앞으로 오셈[SEP]ㅇㅋ[EOS]" inference: parameters: max_length: 64 top_k: 5 --- # BART R3F [2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다. [bart-pretrained](https://huggingface.co/alaggung/bart-pretrained) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다. 데이터는 [AIHub 한국어 대화요약](https://aihub.or.kr/aidata/30714) 데이터를 사용하였습니다.
huang0624/Taiwan_House_Prediction	huang0624	2022-01-11T11:12:21Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	Hi, this is Taiwan_House_Prediction.

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