license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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cc-by-4.0 | ['norwegian', 'bert', 'ner'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-base-ner") model = AutoModelForTokenClassification.from_pretrained("NbAiLab/nb-bert-base-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Jeg heter Kjell og bor i Oslo." ner_results = nlp(example) print(ner_results) ``` | a8b1b3b1bf66f94290554cc4978339ee |
apache-2.0 | ['bert', 'cola', 'glue', 'torchdistill'] | false | `bert-large-uncased` fine-tuned on CoLA dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/cola/ce/bert_large_uncased.yaml). I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**. | b8175ede373ff5212287c4a0c5669aaf |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-cat_oci_spa-en Neural machine translation model for translating from Catalan, Occitan and Spanish (cat+oci+spa) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | 6ac38f5eb5413c660b0d5cf73d5b9591 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): cat spa * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cat+oci+spa-eng/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT cat+oci+spa-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat+oci+spa-eng/README.md) | 5c3ab09244a753dc9155936097c2f889 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "¿Puedo hacerte una pregunta?", "Toca algo de música." ] model_name = "pytorch-models/opus-mt-tc-big-cat_oci_spa-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | d496bab19c39b0c4a4ccaafbb87a12d4 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | He plays some music. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-cat_oci_spa-en") print(pipe("¿Puedo hacerte una pregunta?")) | 7858a39993786413528ea605a6a5f6b6 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat+oci+spa-eng/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cat+oci+spa-eng/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 1c2738681406fd55f582b9379eaa7c00 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | cat-eng | tatoeba-test-v2021-08-07 | 0.72019 | 57.3 | 1631 | 12627 | | spa-eng | tatoeba-test-v2021-08-07 | 0.76017 | 62.3 | 16583 | 138123 | | cat-eng | flores101-devtest | 0.69572 | 45.4 | 1012 | 24721 | | oci-eng | flores101-devtest | 0.63347 | 37.5 | 1012 | 24721 | | spa-eng | flores101-devtest | 0.59696 | 29.9 | 1012 | 24721 | | spa-eng | newssyscomb2009 | 0.57104 | 30.8 | 502 | 11818 | | spa-eng | news-test2008 | 0.55440 | 27.9 | 2051 | 49380 | | spa-eng | newstest2009 | 0.57153 | 30.2 | 2525 | 65399 | | spa-eng | newstest2010 | 0.61890 | 36.8 | 2489 | 61711 | | spa-eng | newstest2011 | 0.60278 | 34.7 | 3003 | 74681 | | spa-eng | newstest2012 | 0.62760 | 38.6 | 3003 | 72812 | | spa-eng | newstest2013 | 0.60994 | 35.3 | 3000 | 64505 | | spa-eng | tico19-test | 0.74033 | 51.8 | 2100 | 56315 | | dc2591e73926d512c65fa72f5da9967b |
mit | ['generated_from_trainer', 'nlu', 'text-classification'] | false | multilingual_minilm-amazon_massive-intent_eu7 This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [MASSIVE 1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluation set: - Loss: 0.8238 - Accuracy: 0.8623 - F1: 0.8623 | 9110d479cb581915f85cd3e793ff730a |
mit | ['generated_from_trainer', 'nlu', 'text-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 1.3523 | 1.0 | 5038 | 1.3058 | 0.6937 | 0.6937 | | 0.7842 | 2.0 | 10076 | 0.8434 | 0.8059 | 0.8059 | | 0.5359 | 3.0 | 15114 | 0.7231 | 0.8302 | 0.8302 | | 0.4106 | 4.0 | 20152 | 0.7121 | 0.8443 | 0.8443 | | 0.3294 | 5.0 | 25190 | 0.7366 | 0.8497 | 0.8497 | | 0.2621 | 6.0 | 30228 | 0.7702 | 0.8528 | 0.8528 | | 0.2164 | 7.0 | 35266 | 0.7773 | 0.8577 | 0.8577 | | 0.1756 | 8.0 | 40304 | 0.8080 | 0.8569 | 0.8569 | | 0.1625 | 9.0 | 45342 | 0.8162 | 0.8624 | 0.8624 | | 0.1448 | 10.0 | 50380 | 0.8238 | 0.8623 | 0.8623 | | 9231f1997f48c5fe45c5a4952d8f7676 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-dpv-finetuned This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - epoch: 13.07 - eval_loss: 0.0002 - eval_runtime: 8695.8511 - eval_samples_per_second: 0.458 - eval_steps_per_second: 0.458 - eval_wer: 0.0112 - step: 13000 | d499af1e4e367c2528f12e5356a0a350 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - 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 | f3182ee67a6da48ef0cdfa15bc246207 |
mit | ['generated_from_trainer'] | false | bert-base-historic-multilingual-64k-td-cased-squad-fr This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-64k-td-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-64k-td-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7419 | 026bce2b2e6f50d94e5c42b6b6ddd653 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9834 | 1.0 | 3569 | 1.8605 | | 1.663 | 2.0 | 7138 | 1.7419 | | 7d7ed4166556b3b565718a7d25a3c676 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-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: 0.5108 - Wer: 0.3342 | 7286ac5d04db1503e8748124337f9955 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6383 | 1.0 | 500 | 2.3747 | 1.0 | | 0.9624 | 2.01 | 1000 | 0.5724 | 0.5213 | | 0.4521 | 3.01 | 1500 | 0.4892 | 0.4794 | | 0.3126 | 4.02 | 2000 | 0.4250 | 0.3991 | | 0.2299 | 5.02 | 2500 | 0.4288 | 0.3929 | | 0.195 | 6.02 | 3000 | 0.4707 | 0.3974 | | 0.1602 | 7.03 | 3500 | 0.4731 | 0.4034 | | 0.1477 | 8.03 | 4000 | 0.4405 | 0.3896 | | 0.1284 | 9.04 | 4500 | 0.4663 | 0.3850 | | 0.1114 | 10.04 | 5000 | 0.4814 | 0.3759 | | 0.1024 | 11.04 | 5500 | 0.4821 | 0.3701 | | 0.0973 | 12.05 | 6000 | 0.4718 | 0.3709 | | 0.0832 | 13.05 | 6500 | 0.5257 | 0.3678 | | 0.0741 | 14.06 | 7000 | 0.4741 | 0.3621 | | 0.0696 | 15.06 | 7500 | 0.5073 | 0.3710 | | 0.0664 | 16.06 | 8000 | 0.4886 | 0.3651 | | 0.0613 | 17.07 | 8500 | 0.5300 | 0.3588 | | 0.0612 | 18.07 | 9000 | 0.4983 | 0.3543 | | 0.049 | 19.08 | 9500 | 0.5158 | 0.3592 | | 0.0455 | 20.08 | 10000 | 0.5213 | 0.3525 | | 0.042 | 21.08 | 10500 | 0.4979 | 0.3474 | | 0.0376 | 22.09 | 11000 | 0.5335 | 0.3493 | | 0.0331 | 23.09 | 11500 | 0.5276 | 0.3451 | | 0.0346 | 24.1 | 12000 | 0.5106 | 0.3428 | | 0.0294 | 25.1 | 12500 | 0.5414 | 0.3426 | | 0.0265 | 26.1 | 13000 | 0.5234 | 0.3363 | | 0.0273 | 27.11 | 13500 | 0.5207 | 0.3356 | | 0.0255 | 28.11 | 14000 | 0.5092 | 0.3354 | | 0.0248 | 29.12 | 14500 | 0.5108 | 0.3342 | | f56859379225c84a9e9667ec1af5f5c6 |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/distilbert-base-uncased-mlm-electronics-attribute-correction-qa-mlm This model is a fine-tuned version of [ksabeh/distilbert-base-uncased-mlm-electronics](https://huggingface.co/ksabeh/distilbert-base-uncased-mlm-electronics) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0607 - Validation Loss: 0.0609 - Epoch: 1 | 1a9ceaaa21380daf31846915fbce7bf4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36794, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | e45f06f27b4dab6a0115be06cc416836 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qqp_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5363 - Accuracy: 0.7995 - F1: 0.7338 - Combined Score: 0.7666 | 8e8c1d67e4e664114c61184c954af243 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Combined Score | F1 | Validation Loss | |:-------------:|:-----:|:------:|:--------:|:--------------:|:------:|:---------------:| | 0.3538 | 1.0 | 29671 | 0.7995 | 0.7666 | 0.7338 | 0.5363 | | 0.1571 | 2.0 | 59342 | 0.7215 | 0.8000 | 0.7396 | 0.7698 | | 0.0894 | 3.0 | 89013 | 0.7922 | 0.7998 | 0.7407 | 0.7702 | | 0.0596 | 4.0 | 118684 | 0.8829 | 0.8045 | 0.7399 | 0.7722 | | 0.0433 | 5.0 | 148355 | 0.8505 | 0.8110 | 0.7443 | 0.7777 | | 0.0334 | 6.0 | 178026 | 1.0843 | 0.8047 | 0.7446 | 0.7746 | | d633a5831c490a3e6036e7a9b6a729f4 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_vp-100k_s842 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 784d3bfcb703489ee48ad78594a8ee6d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP | ff97d13dfc0427fe137136787104765c |
mit | ['generated_from_trainer'] | false | Train ```bash python run_qa.py \ --model_name_or_path roberta-large \ --dataset_name squad \ --do_eval \ --do_train \ --evaluation_strategy steps \ --eval_steps 500 \ --learning_rate 3e-5 \ --fp16 \ --num_train_epochs 2 \ --per_device_eval_batch_size 64 \ --per_device_train_batch_size 16 \ --max_seq_length 384 \ --doc_stride 128 \ --save_steps 1000 \ --logging_steps 1 \ --overwrite_output_dir \ --run_name $RUNID \ --output_dir $OUTDIR ``` | 16f2f9f15a699a16fde1f13aa1952702 |
mit | ['generated_from_trainer'] | false | Eval ```bash export CUDA_VISIBLE_DEVICES=0 MODEL=vuiseng9/roberta-l-squadv1.1 OUTDIR=eval-$(basename $MODEL) WORKDIR=transformers/examples/pytorch/question-answering cd $WORKDIR nohup python run_qa.py \ --model_name_or_path $MODEL \ --dataset_name squad \ --do_eval \ --per_device_eval_batch_size 16 \ --max_seq_length 384 \ --doc_stride 128 \ --overwrite_output_dir \ --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log & ``` ```bash eval_exact_match = 88.4674 eval_f1 = 94.3001 eval_samples = 10790 ``` | fc1302745f6f6d334f995fc144f298ff |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_xlsr-53_s303 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | f7cb7f198fca36d3141cffbe0d5f333e |
apache-2.0 | ['translation'] | false | opus-mt-es-ru * source languages: es * target languages: ru * OPUS readme: [es-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ru/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-ru/opus-2020-01-20.eval.txt) | 841330209bc4c136361aa73a2ce98212 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.es.ru | 20.9 | 0.489 | | newstest2013.es.ru | 23.4 | 0.504 | | Tatoeba.es.ru | 47.0 | 0.657 | | 203524ead4ffcefb680e21757e27c477 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes_movie_review dataset. It achieves the following results on the evaluation set: - Loss: 0.8692 - Accuracy: 0.8433 - F1: 0.8407 | d6d6a4267d2779a0ff74e6eec15743e4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec-base-All 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: 3.0545 - Wer: 0.8861 - Cer: 0.5014 | cf7383243027a263b661965d57d0d21d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 120 - mixed_precision_training: Native AMP | bdada70946f46d7b74ca77655b717a7c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | No log | 3.33 | 500 | 4.0654 | 1.0 | 0.9823 | | No log | 6.67 | 1000 | 3.4532 | 1.0 | 0.9823 | | No log | 10.0 | 1500 | 3.0707 | 0.9992 | 0.9781 | | No log | 13.33 | 2000 | 2.7335 | 1.0017 | 0.9027 | | No log | 16.67 | 2500 | 2.5896 | 1.0690 | 0.7302 | | No log | 20.0 | 3000 | 2.3315 | 1.0690 | 0.6677 | | No log | 23.33 | 3500 | 2.2217 | 1.0150 | 0.5966 | | No log | 26.67 | 4000 | 2.3802 | 1.0549 | 0.5948 | | No log | 30.0 | 4500 | 2.2208 | 0.9975 | 0.5681 | | 2.4224 | 33.33 | 5000 | 2.2687 | 0.9800 | 0.5537 | | 2.4224 | 36.67 | 5500 | 2.3169 | 0.9476 | 0.5493 | | 2.4224 | 40.0 | 6000 | 2.5196 | 0.9900 | 0.5509 | | 2.4224 | 43.33 | 6500 | 2.4816 | 0.9501 | 0.5272 | | 2.4224 | 46.67 | 7000 | 2.4894 | 0.9485 | 0.5276 | | 2.4224 | 50.0 | 7500 | 2.4555 | 0.9418 | 0.5305 | | 2.4224 | 53.33 | 8000 | 2.7326 | 0.9559 | 0.5255 | | 2.4224 | 56.67 | 8500 | 2.5514 | 0.9227 | 0.5209 | | 2.4224 | 60.0 | 9000 | 2.9135 | 0.9717 | 0.5455 | | 2.4224 | 63.33 | 9500 | 3.0465 | 0.8346 | 0.5002 | | 0.8569 | 66.67 | 10000 | 2.8177 | 0.9302 | 0.5216 | | 0.8569 | 70.0 | 10500 | 2.9908 | 0.9310 | 0.5128 | | 0.8569 | 73.33 | 11000 | 3.1752 | 0.9235 | 0.5284 | | 0.8569 | 76.67 | 11500 | 2.7412 | 0.8886 | 0.5 | | 0.8569 | 80.0 | 12000 | 2.7362 | 0.9127 | 0.5040 | | 0.8569 | 83.33 | 12500 | 2.9636 | 0.9152 | 0.5093 | | 0.8569 | 86.67 | 13000 | 3.0139 | 0.9011 | 0.5097 | | 0.8569 | 90.0 | 13500 | 2.8325 | 0.8853 | 0.5032 | | 0.8569 | 93.33 | 14000 | 3.0383 | 0.8845 | 0.5056 | | 0.8569 | 96.67 | 14500 | 2.7931 | 0.8795 | 0.4965 | | 0.3881 | 100.0 | 15000 | 2.8972 | 0.8928 | 0.5012 | | 0.3881 | 103.33 | 15500 | 2.7780 | 0.8736 | 0.4947 | | 0.3881 | 106.67 | 16000 | 3.1081 | 0.9036 | 0.5109 | | 0.3881 | 110.0 | 16500 | 3.0078 | 0.8928 | 0.5032 | | 0.3881 | 113.33 | 17000 | 3.0245 | 0.8886 | 0.5009 | | 0.3881 | 116.67 | 17500 | 3.0739 | 0.8928 | 0.5065 | | 0.3881 | 120.0 | 18000 | 3.0545 | 0.8861 | 0.5014 | | db1c13782ce9262c75d64b4d18b170e3 |
apache-2.0 | ['translation'] | false | Model description
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Bleu: 40.70
More information needed
| 87ea98ef8b05b3922109950b2edeee86 |
apache-2.0 | ['translation'] | false | 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
- mixed_precision_training: Native AMP
| 4348705f85f226b06e31cdf848a4785e |
apache-2.0 | ['generated_from_keras_callback'] | false | HanSSH/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2684 - Validation Loss: 3.2288 - Epoch: 3 | d24c887f0d65cea559caf8d5734a571f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.00056, 'decay_steps': 4836, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 0262b354fa1acb6a48615c10662f8182 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.8144 | 3.5283 | 0 | | 3.8758 | 3.2971 | 1 | | 3.4741 | 3.2452 | 2 | | 3.2684 | 3.2288 | 3 | | e8cd4ba75def0ce0f6d0e31fe036d9e5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2 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: 0.3739 - Accuracy: 0.9128 | 6c9a54a4194a9295beb64b6b6cf0e7f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1885 | 1.0 | 4210 | 0.3092 | 0.9083 | | 0.1311 | 2.0 | 8420 | 0.3809 | 0.9071 | | 0.1036 | 3.0 | 12630 | 0.3739 | 0.9128 | | 0.0629 | 4.0 | 16840 | 0.4623 | 0.9083 | | 0.036 | 5.0 | 21050 | 0.5198 | 0.9048 | | 4c940e330d45bf4c5f34400de270c8eb |
apache-2.0 | ['Quality Estimation', 'siamesetransquest', 'da'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-ro_en-wiki") predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ``` | 1ba7300c010981228ed81e3db30bb179 |
mit | ['generated_from_trainer'] | false | PubMedBert-abstract-cord19-v2 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 the [pritamdeka/cord-19-abstract](https://huggingface.co/datasets/pritamdeka/cord-19-abstract) dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Accuracy: 0.7247 | a76cf904b39dedc9a4e1bbf4799f7b57 |
mit | ['generated_from_trainer'] | false | 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=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 4.0 - mixed_precision_training: Native AMP | 428c15931e1e4bf025306ed821e0e5d0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.27 | 0.53 | 5000 | 1.2425 | 0.7236 | | 1.2634 | 1.06 | 10000 | 1.3123 | 0.7141 | | 1.3041 | 1.59 | 15000 | 1.3583 | 0.7072 | | 1.3829 | 2.12 | 20000 | 1.3590 | 0.7121 | | 1.3069 | 2.65 | 25000 | 1.3506 | 0.7154 | | 1.2921 | 3.18 | 30000 | 1.3448 | 0.7160 | | 1.2731 | 3.7 | 35000 | 1.3375 | 0.7178 | | a6b611dddac622e992f72d5630a0fc52 |
mit | ['generated_from_trainer'] | false | roberta-large-unlabeled-gab-reddit-semeval2023-task10-57000sample This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8874 | c87fa783cd5e47b0033d2d6dea6e620a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | aef32128bf736527755eda4334964ccc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1999 | 1.0 | 3563 | 2.0576 | | 2.0587 | 2.0 | 7126 | 1.9371 | | 1.9591 | 3.0 | 10689 | 1.8823 | | 1.8652 | 4.0 | 14252 | 1.8874 | | 26f4605333151fb50b852aece2258ff1 |
apache-2.0 | ['tapas', 'table-question-answering'] | false | TAPAS large model fine-tuned on WikiTable Questions (WTQ) This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is: - `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_large` (intermediate pre-training, absolute position embeddings). Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 041fc6d93702c19d16144c1dd268010b |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Results Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- **LARGE** | **noreset** | **0.5062** | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) **LARGE** | **reset** | **0.5097** | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main) BASE | noreset | 0.4525 | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset) BASE | reset | 0.4638 | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main) MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset) MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main) SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset) SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main) MINI | noreset | 0.2783 | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset) MINI | reset | 0.2854 | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main) TINY | noreset | 0.0823 | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset) TINY | reset | 0.1039 | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main) | a9e5d366fd6b2e201f4601ef29c4d125 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_mnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0985 - Accuracy: 0.3522 | 5ad93e7b70ce670c6b7a3f1e80c27f97 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0988 | 1.0 | 3068 | 1.0988 | 0.3182 | | 1.0987 | 2.0 | 6136 | 1.0986 | 0.3184 | | 1.0987 | 3.0 | 9204 | 1.0989 | 0.3274 | | 1.0987 | 4.0 | 12272 | 1.0987 | 0.3182 | | 1.0987 | 5.0 | 15340 | 1.0984 | 0.3545 | | 1.0986 | 6.0 | 18408 | 1.0987 | 0.3274 | | 1.0986 | 7.0 | 21476 | 1.0993 | 0.3274 | | 1.0986 | 8.0 | 24544 | 1.0985 | 0.3545 | | 1.0986 | 9.0 | 27612 | 1.0985 | 0.3545 | | 1.0986 | 10.0 | 30680 | 1.0987 | 0.3182 | | 0a982cb475006b8cec8be1621a6fadcf |
gpl-2.0 | ['corenlp'] | false | Core NLP model for english-extra CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP). This card and repo were automatically prepared with `hugging_corenlp.py` in the `stanfordnlp/huggingface-models` repo Last updated 2023-01-21 01:36:25.611 | ee5bc5be3e30b3edc1a3f88617ac40de |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | lacroix_can_plus_van_gogh Dreambooth model trained by spooncats with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:  | 778d25df0b1e78f16c5ffb4307b4e368 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-jaquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | a7bf2eff3f42942a6f85d223df0af7d0 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** ja - **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 5ec06ba90c37cf2ea4dea48c53bf237b |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-jaquad-ae") output = pipe("『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。") ``` | ec1b8982d1567ac4159e0cf4c14e95df |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 28.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | AnswerF1Score | 28.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | BERTScore | 77.33 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_1 | 33.75 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_2 | 30.74 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_3 | 28.29 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_4 | 26.48 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | METEOR | 25.61 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | MoverScore | 64.96 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | ROUGE_L | 35.58 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | 5e0028168536a7aed387be9262af9952 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_jaquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 9 - batch: 8 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-jaquad-ae/raw/main/trainer_config.json). | ead771333e23a3236237a33d67b42be2 |
mit | ['classification'] | false | inference_nsmc.py ```python import json import sys import logging import torch from torch import nn from transformers import ElectraConfig from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification logging.basicConfig( level=logging.INFO, format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s', handlers=[ logging.FileHandler(filename='tmp.log'), logging.StreamHandler(sys.stdout) ] ) logger = logging.getLogger(__name__) max_seq_length = 128 classes = ['Neg', 'Pos'] tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def model_fn(model_path=None): | 8d55b31dc29182e3b30c715246b28196 |
mit | ['classification'] | false | Download model from the Huggingface hub model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc') model.to(device) return model def input_fn(input_data, content_type="application/jsonlines"): data_str = input_data.decode("utf-8") jsonlines = data_str.split("\n") transformed_inputs = [] for jsonline in jsonlines: text = json.loads(jsonline)["text"][0] logger.info("input text: {}".format(text)) encode_plus_token = tokenizer.encode_plus( text, max_length=max_seq_length, add_special_tokens=True, return_token_type_ids=False, padding="max_length", return_attention_mask=True, return_tensors="pt", truncation=True, ) transformed_inputs.append(encode_plus_token) return transformed_inputs def predict_fn(transformed_inputs, model): predicted_classes = [] for data in transformed_inputs: data = data.to(device) output = model(**data) softmax_fn = nn.Softmax(dim=1) softmax_output = softmax_fn(output[0]) _, prediction = torch.max(softmax_output, dim=1) predicted_class_idx = prediction.item() predicted_class = classes[predicted_class_idx] score = softmax_output[0][predicted_class_idx] logger.info("predicted_class: {}".format(predicted_class)) prediction_dict = {} prediction_dict["predicted_label"] = predicted_class prediction_dict['score'] = score.cpu().detach().numpy().tolist() jsonline = json.dumps(prediction_dict) logger.info("jsonline: {}".format(jsonline)) predicted_classes.append(jsonline) predicted_classes_jsonlines = "\n".join(predicted_classes) return predicted_classes_jsonlines def output_fn(outputs, accept="application/jsonlines"): return outputs, accept ``` | 094024285cf0a75a8b1fb9697e265ca9 |
mit | ['classification'] | false | test.py ```python >>> from inference_nsmc import model_fn, input_fn, predict_fn, output_fn >>> with open('samples/nsmc.txt', mode='rb') as file: >>> model_input_data = file.read() >>> model = model_fn() >>> transformed_inputs = input_fn(model_input_data) >>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model) >>> model_outputs = output_fn(predicted_classes_jsonlines) >>> print(model_outputs[0]) [{inference_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다 [{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다 [{inference_nsmc.py:77} INFO - predicted_class: Pos [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613} [{inference_nsmc.py:77} INFO - predicted_class: Neg [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967} {"predicted_label": "Pos", "score": 0.9619030952453613} {"predicted_label": "Neg", "score": 0.9994170665740967} ``` | 54f705951112cf04d7953f7c84f72669 |
apache-2.0 | ['vision', 'image-classification'] | false | RegNet RegNet model trained on imagenet-1k. It was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. | cd7e80c3ba5c4a5bf435ae9ab5e9bd35 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on a billion uncurated Instagram images. This model is later fine-tuned on ImageNet.  | baf1520e8db9ca3664738a776264ee5f |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-320-seer-in1k") >>> model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-320-seer-in1k") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> | 198e4e12693578b232ac58fa6d19f6dc |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) 'tabby, tabby cat' ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet). | 7028894206cbaa8aa274713838884c14 |
mit | ['question-answering', 'generated_from_trainer', 'bert', 'jaquad'] | false | roberta_qa_japanese (Japanese caption : 日本語の (抽出型) 質問応答のモデル) This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) (pre-trained RoBERTa model provided by rinna Co., Ltd.) trained for extractive question answering. The model is fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD) dataset provided by Skelter Labs, in which data is collected from Japanese Wikipedia articles and annotated by a human. | 03571aebf04806ececcc3802e56bbe87 |
mit | ['question-answering', 'generated_from_trainer', 'bert', 'jaquad'] | false | Intended uses When running with a dedicated pipeline : ```python from transformers import pipeline model_name = "tsmatz/roberta_qa_japanese" qa_pipeline = pipeline( "question-answering", model=model_name, tokenizer=model_name) result = qa_pipeline( question = "決勝トーナメントで日本に勝ったのはどこでしたか。", context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。", align_to_words = False, ) print(result) ``` When manually running through forward pass : ```python import torch import numpy as np from transformers import AutoModelForQuestionAnswering, AutoTokenizer model_name = "tsmatz/roberta_qa_japanese" model = (AutoModelForQuestionAnswering .from_pretrained(model_name)) tokenizer = AutoTokenizer.from_pretrained(model_name) def inference_answer(question, context): question = question context = context test_feature = tokenizer( question, context, max_length=318, ) with torch.no_grad(): outputs = model(torch.tensor([test_feature["input_ids"]])) start_logits = outputs.start_logits.cpu().numpy() end_logits = outputs.end_logits.cpu().numpy() answer_ids = test_feature["input_ids"][np.argmax(start_logits):np.argmax(end_logits)+1] return "".join(tokenizer.batch_decode(answer_ids)) question = "決勝トーナメントで日本に勝ったのはどこでしたか。" context = "日本は予選リーグで強豪のドイツとスペインに勝って決勝トーナメントに進んだが、クロアチアと対戦して敗れた。" answer_pred = inference_answer(question, context) print(answer_pred) ``` | 36025330b6059c525dc59b431ed0e6f9 |
mit | ['question-answering', 'generated_from_trainer', 'bert', 'jaquad'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - 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: 100 - num_epochs: 3 | 9935c386d516477df08648628fb720ee |
mit | ['question-answering', 'generated_from_trainer', 'bert', 'jaquad'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1293 | 0.13 | 150 | 1.0311 | | 1.1965 | 0.26 | 300 | 0.6723 | | 1.022 | 0.39 | 450 | 0.4838 | | 0.9594 | 0.53 | 600 | 0.5174 | | 0.9187 | 0.66 | 750 | 0.4671 | | 0.8229 | 0.79 | 900 | 0.4650 | | 0.71 | 0.92 | 1050 | 0.2648 | | 0.5436 | 1.05 | 1200 | 0.2665 | | 0.5045 | 1.19 | 1350 | 0.2686 | | 0.5025 | 1.32 | 1500 | 0.2082 | | 0.5213 | 1.45 | 1650 | 0.1715 | | 0.4648 | 1.58 | 1800 | 0.1563 | | 0.4698 | 1.71 | 1950 | 0.1488 | | 0.4823 | 1.84 | 2100 | 0.1050 | | 0.4482 | 1.97 | 2250 | 0.0821 | | 0.2755 | 2.11 | 2400 | 0.0898 | | 0.2834 | 2.24 | 2550 | 0.0964 | | 0.2525 | 2.37 | 2700 | 0.0533 | | 0.2606 | 2.5 | 2850 | 0.0561 | | 0.2467 | 2.63 | 3000 | 0.0601 | | 0.2799 | 2.77 | 3150 | 0.0562 | | 0.2497 | 2.9 | 3300 | 0.0516 | | ae2d4dd4c4a0c6267be64d29b96a8e02 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2258 - Accuracy: 0.9245 - F1: 0.9248 | 542df236c11b5ffca836fd7c0300a919 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3316 | 0.901 | 0.8967 | | 0.2584 | 2.0 | 500 | 0.2258 | 0.9245 | 0.9248 | | 5a1b131a9af14f8865f1de3381737474 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 2383239655d9d1d305c52878023fa8f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 334 | 0.1500 | 24.5024 | 21.4979 | 24.0227 | 24.0303 | 19.0 | | b28fe2d7c2861f1c6be28a50effc597d |
mit | ['conversational'] | false | How to use ```python from transformers import OpenAIGPTLMHeadModel, GPT2LMHeadModel, BertTokenizer import torch tokenizer = BertTokenizer.from_pretrained("thu-coai/CDial-GPT2_LCCC-base") model = GPT2LMHeadModel.from_pretrained("thu-coai/CDial-GPT2_LCCC-base") ``` For more details, please refer to our [repo.](https://github.com/thu-coai/CDial-GPT) on github. | ae536100804fcbc68d379e6191e1bd83 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-query This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3668 - Accuracy: 0.8936 - F1: 0.8924 | bb9d379f250d594ac5939f6dce833acc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | b059eb55b037dfe3d76cec041143ee77 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6511 | 1.0 | 30 | 0.5878 | 0.7234 | 0.6985 | | 0.499 | 2.0 | 60 | 0.4520 | 0.8723 | 0.8683 | | 0.3169 | 3.0 | 90 | 0.3668 | 0.8936 | 0.8924 | | b5577323b62fa1caf72d038242c63cf7 |
apache-2.0 | ['generated_from_trainer'] | false | test_ner-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Precision: 0.9242 - Recall: 0.9349 - F1: 0.9295 - Accuracy: 0.9834 | 107618a3cfb587f6104d8c85afac15ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2385 | 1.0 | 878 | 0.0708 | 0.9140 | 0.9216 | 0.9178 | 0.9808 | | 0.055 | 2.0 | 1756 | 0.0626 | 0.9209 | 0.9340 | 0.9274 | 0.9828 | | 0.0309 | 3.0 | 2634 | 0.0623 | 0.9242 | 0.9349 | 0.9295 | 0.9834 | | d6d887094e8c07397bf89f3941e70256 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Western_Armenian (hyw) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:32:11.573 | 1ad8aeff121610b7d67530b77b2dae83 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/tedlium2_transducer_e_branchformer` This model was trained by Yifan Peng using tedlium2 recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html) | 1547b8b5eb2307a89b0ea9bbc5e9e20e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 478ba004e114e7862b05fb01112de7f7e1da3996 pip install -e . cd egs2/tedlium2/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/tedlium2_transducer_e_branchformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 46b272c7054671eee9ad4f8bb437a5fc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Feb 9 01:29:33 CST 2023` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.13.1` - Git hash: `478ba004e114e7862b05fb01112de7f7e1da3996` - Commit date: `Tue Feb 7 00:50:49 2023 +0000` | b31d347b6543e66b5ca955d728683e9e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|14671|93.4|4.3|2.3|1.0|7.6|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|27500|93.6|4.0|2.4|1.0|7.4|63.5| | 1c918dfb39e1a212f8e7c9996c61d3d1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|78259|97.1|0.9|2.0|0.9|3.8|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|145066|97.1|0.9|2.1|0.9|3.9|63.5| | 822b2f5f9042c314e05972fcc536ec0d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_transducer_asr_model_valid.loss.ave/dev|466|28296|94.7|3.1|2.3|0.8|6.2|71.7| |decode_asr_transducer_asr_model_valid.loss.ave/test|1155|52113|95.1|2.6|2.2|0.9|5.8|63.5| | c25ec079bfb9d8aa85ecb5dbe979086b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transducer_e_branchformer_e12.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transducer_e_branchformer_e12_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 6 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45753 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 5 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 10000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - s - ▁the - t - ▁a - ▁and - ▁to - d - e - ▁of - '''' - n - ing - ▁in - ▁i - ▁that - i - a - l - p - m - y - o - ▁it - ▁we - c - u - ▁you - ed - ▁ - r - ▁is - re - ▁this - ar - g - ▁so - al - b - ▁s - or - ▁f - ▁c - in - k - f - ▁for - ic - er - le - ▁be - ▁do - ▁re - ve - ▁e - ▁w - ▁was - es - ▁they - ly - h - ▁on - v - ▁are - ri - ▁have - an - ▁what - ▁with - ▁t - w - ur - it - ent - ▁can - ▁he - ▁but - ra - ce - ▁me - ▁b - ▁ma - ▁p - ll - ▁st - ▁one - 'on' - ▁about - th - ▁de - en - ▁all - ▁not - il - ▁g - ch - at - ▁there - ▁mo - ter - ation - tion - ▁at - ▁my - ro - ▁as - te - ▁le - ▁con - ▁like - ▁people - ▁or - ▁an - el - ▁if - ▁from - ver - ▁su - ▁co - ate - ▁these - ol - ci - ▁now - ▁see - ▁out - ▁our - ion - ▁know - ect - ▁just - as - ▁ex - ▁ch - ▁d - ▁when - ▁very - ▁think - ▁who - ▁because - ▁go - ▁up - ▁us - ▁pa - ▁no - ies - ▁di - ▁ho - om - ive - ▁get - id - ▁o - ▁hi - un - ▁how - ▁by - ir - et - ck - ity - ▁po - ul - ▁which - ▁mi - ▁some - z - ▁sp - ▁un - ▁going - ▁pro - ist - ▁se - ▁look - ▁time - ment - de - ▁more - ▁had - ng - ▁would - ge - la - ▁here - ▁really - x - ▁your - ▁them - us - me - ▁en - ▁two - ▁k - ▁li - ▁world - ne - ow - ▁way - ▁want - ▁work - ▁don - ▁lo - ▁fa - ▁were - ▁their - age - vi - ▁ha - ac - der - est - ▁bo - am - ▁other - able - ▁actually - ▁sh - ▁make - ▁ba - ▁la - ine - ▁into - ▁where - ▁could - ▁comp - ting - ▁has - ▁will - ▁ne - j - ical - ally - ▁vi - ▁things - ▁te - igh - ▁say - ▁years - ers - ▁ra - ther - ▁than - ru - ▁ro - op - ▁did - ▁any - ▁new - ound - ig - ▁well - mo - ▁she - ▁na - ▁been - he - ▁thousand - ▁car - ▁take - ▁right - ▁then - ▁need - ▁start - ▁hundred - ▁something - ▁over - ▁com - ia - ▁kind - um - if - ▁those - ▁first - ▁pre - ta - ▁said - ize - end - ▁even - ▁thing - one - ▁back - ite - ▁every - ▁little - ry - ▁life - ▁much - ke - ▁also - ▁most - ant - per - ▁three - ▁come - ▁lot - ance - ▁got - ▁talk - ▁per - ▁inter - ▁sa - ▁use - ▁mu - ▁part - ish - ence - ▁happen - ▁bi - ▁mean - ough - ▁qu - ▁bu - ▁day - ▁ga - ▁only - ▁many - ▁different - ▁dr - ▁th - ▁show - ful - ▁down - ated - ▁good - ▁tra - ▁around - ▁idea - ▁human - ous - ▁put - ▁through - ▁five - ▁why - ▁change - ▁real - ff - ible - ▁fact - ▁same - ▁jo - ▁live - ▁year - ▁problem - ▁ph - ▁four - ▁give - ▁big - ▁tell - ▁great - ▁try - ▁va - ▁ru - ▁system - ▁six - ▁plan - ▁place - ▁build - ▁called - ▁again - ▁point - ▁twenty - ▁percent - ▁nine - ▁find - ▁app - ▁after - ▁long - ▁eight - ▁imp - ▁gene - ▁design - ▁today - ▁should - ▁made - ious - ▁came - ▁learn - ▁last - ▁own - way - ▁turn - ▁seven - ▁high - ▁question - ▁person - ▁brain - ▁important - ▁another - ▁thought - ▁trans - ▁create - ness - ▁hu - ▁power - ▁act - land - ▁play - ▁sort - ▁old - ▁before - ▁course - ▁understand - ▁feel - ▁might - ▁each - ▁million - ▁better - ▁together - ▁ago - ▁example - ▁help - ▁story - ▁next - ▁hand - ▁school - ▁water - ▁develop - ▁technology - que - ▁second - ▁grow - ▁still - ▁cell - ▁believe - ▁number - ▁small - ▁between - qui - ▁data - ▁become - ▁america - ▁maybe - ▁space - ▁project - ▁organ - ▁vo - ▁children - ▁book - graph - ▁open - ▁fifty - ▁picture - ▁health - ▁thirty - ▁africa - ▁reason - ▁large - ▁hard - ▁computer - ▁always - ▁sense - ▁money - ▁women - ▁everything - ▁information - ▁country - ▁teach - ▁energy - ▁experience - ▁food - ▁process - qua - ▁interesting - ▁future - ▁science - q - '0' - '5' - '6' - '9' - '3' - '8' - '4' - N - A - '7' - S - G - F - R - L - U - E - T - H - _ - B - D - J - M - ă - ō - ť - '2' - '-' - '1' - C - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: joint_space_size: 320 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 report_cer: false report_wer: false preencoder: null preencoder_conf: {} encoder: e_branchformer encoder_conf: output_size: 256 attention_heads: 4 attention_layer_type: rel_selfattn pos_enc_layer_type: rel_pos rel_pos_type: latest cgmlp_linear_units: 1024 cgmlp_conv_kernel: 31 use_linear_after_conv: false gate_activation: identity num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d layer_drop_rate: 0.0 linear_units: 1024 positionwise_layer_type: linear use_ffn: true macaron_ffn: true merge_conv_kernel: 31 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 256 dropout: 0.1 dropout_embed: 0.2 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202301' distributed: true ``` </details> | e5e590bdbb0790158a27b29d3e0f2ded |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-230119-100058 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4305 - Mae: 0.4203 - Rmse: 0.6123 - Abs Rel: 0.4280 - Log Mae: 0.1694 - Log Rmse: 0.2214 - Delta1: 0.3813 - Delta2: 0.6446 - Delta3: 0.8152 | a18e462204edbb03ccb3a0ed7f32093f |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 75 - mixed_precision_training: Native AMP | 5e02d0b2d137b67e0749e071b4c540fe |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.2807 | 1.0 | 72 | 0.9866 | 0.8312 | 1.0131 | 0.7179 | 0.5655 | 0.5924 | 0.0087 | 0.0200 | 0.0552 | | 0.7396 | 2.0 | 144 | 0.4976 | 0.4741 | 0.6670 | 0.5279 | 0.1989 | 0.2567 | 0.3070 | 0.5470 | 0.7943 | | 0.5018 | 3.0 | 216 | 0.4811 | 0.4630 | 0.6367 | 0.5198 | 0.1929 | 0.2446 | 0.3211 | 0.5440 | 0.7506 | | 0.482 | 4.0 | 288 | 0.4726 | 0.4556 | 0.6337 | 0.4951 | 0.1893 | 0.2410 | 0.3306 | 0.5636 | 0.7663 | | 0.4874 | 5.0 | 360 | 0.4813 | 0.4662 | 0.6355 | 0.5265 | 0.1941 | 0.2446 | 0.3179 | 0.5385 | 0.7278 | | 0.4648 | 6.0 | 432 | 0.4681 | 0.4512 | 0.6309 | 0.4783 | 0.1869 | 0.2383 | 0.3430 | 0.5757 | 0.7527 | | 0.4346 | 7.0 | 504 | 0.4637 | 0.4499 | 0.6292 | 0.4710 | 0.1859 | 0.2357 | 0.3453 | 0.5671 | 0.7644 | | 0.4018 | 8.0 | 576 | 0.4790 | 0.4638 | 0.6349 | 0.5161 | 0.1928 | 0.2436 | 0.3255 | 0.5408 | 0.7338 | | 0.4092 | 9.0 | 648 | 0.4559 | 0.4449 | 0.6267 | 0.4540 | 0.1827 | 0.2319 | 0.3541 | 0.5814 | 0.7692 | | 0.3891 | 10.0 | 720 | 0.4619 | 0.4433 | 0.6259 | 0.4748 | 0.1823 | 0.2351 | 0.3579 | 0.5870 | 0.7742 | | 0.3707 | 11.0 | 792 | 0.4624 | 0.4500 | 0.6269 | 0.4828 | 0.1851 | 0.2350 | 0.3421 | 0.5672 | 0.7638 | | 0.4129 | 12.0 | 864 | 0.4648 | 0.4468 | 0.6265 | 0.4836 | 0.1836 | 0.2358 | 0.3533 | 0.5786 | 0.7625 | | 0.4108 | 13.0 | 936 | 0.4474 | 0.4312 | 0.6187 | 0.4501 | 0.1752 | 0.2280 | 0.3801 | 0.6088 | 0.7887 | | 0.3948 | 14.0 | 1008 | 0.4619 | 0.4498 | 0.6263 | 0.4853 | 0.1844 | 0.2344 | 0.3401 | 0.5721 | 0.7645 | | 0.4009 | 15.0 | 1080 | 0.4619 | 0.4440 | 0.6244 | 0.4889 | 0.1820 | 0.2351 | 0.3563 | 0.5841 | 0.7751 | | 0.3657 | 16.0 | 1152 | 0.4636 | 0.4491 | 0.6260 | 0.4936 | 0.1846 | 0.2360 | 0.3422 | 0.5734 | 0.7644 | | 0.3605 | 17.0 | 1224 | 0.4353 | 0.4255 | 0.6153 | 0.4248 | 0.1715 | 0.2218 | 0.3844 | 0.6207 | 0.8008 | | 0.3937 | 18.0 | 1296 | 0.4756 | 0.4609 | 0.6310 | 0.5281 | 0.1909 | 0.2423 | 0.3220 | 0.5461 | 0.7538 | | 0.3453 | 19.0 | 1368 | 0.4698 | 0.4517 | 0.6270 | 0.5145 | 0.1863 | 0.2392 | 0.3360 | 0.5702 | 0.7689 | | 0.3883 | 20.0 | 1440 | 0.4349 | 0.4240 | 0.6145 | 0.4311 | 0.1712 | 0.2230 | 0.3841 | 0.6321 | 0.8030 | | 0.3482 | 21.0 | 1512 | 0.4339 | 0.4209 | 0.6146 | 0.4223 | 0.1694 | 0.2223 | 0.3967 | 0.6337 | 0.8036 | | 0.3374 | 22.0 | 1584 | 0.4400 | 0.4289 | 0.6167 | 0.4431 | 0.1737 | 0.2254 | 0.3743 | 0.6191 | 0.7971 | | 0.3516 | 23.0 | 1656 | 0.4395 | 0.4280 | 0.6171 | 0.4426 | 0.1737 | 0.2259 | 0.3710 | 0.6241 | 0.7998 | | 0.3901 | 24.0 | 1728 | 0.4444 | 0.4324 | 0.6184 | 0.4562 | 0.1758 | 0.2280 | 0.3665 | 0.6118 | 0.7991 | | 0.3587 | 25.0 | 1800 | 0.4326 | 0.4200 | 0.6129 | 0.4281 | 0.1690 | 0.2222 | 0.3920 | 0.6403 | 0.8073 | | 0.3425 | 26.0 | 1872 | 0.4371 | 0.4231 | 0.6152 | 0.4341 | 0.1709 | 0.2242 | 0.3852 | 0.6372 | 0.7974 | | 0.3252 | 27.0 | 1944 | 0.4381 | 0.4225 | 0.6140 | 0.4399 | 0.1705 | 0.2245 | 0.3851 | 0.6396 | 0.8065 | | 0.3586 | 28.0 | 2016 | 0.4441 | 0.4304 | 0.6162 | 0.4488 | 0.1746 | 0.2258 | 0.3674 | 0.6179 | 0.7929 | | 0.3389 | 29.0 | 2088 | 0.4240 | 0.4112 | 0.6100 | 0.4017 | 0.1640 | 0.2173 | 0.4152 | 0.6599 | 0.8128 | | 0.3418 | 30.0 | 2160 | 0.4312 | 0.4195 | 0.6126 | 0.4211 | 0.1687 | 0.2206 | 0.3899 | 0.6435 | 0.8123 | | 0.3454 | 31.0 | 2232 | 0.4301 | 0.4176 | 0.6126 | 0.4167 | 0.1674 | 0.2203 | 0.3974 | 0.6479 | 0.8089 | | 0.3499 | 32.0 | 2304 | 0.4262 | 0.4154 | 0.6115 | 0.4081 | 0.1661 | 0.2184 | 0.3997 | 0.6578 | 0.8083 | | 0.3649 | 33.0 | 2376 | 0.4429 | 0.4313 | 0.6171 | 0.4507 | 0.1753 | 0.2263 | 0.3641 | 0.6134 | 0.7982 | | 0.3341 | 34.0 | 2448 | 0.4292 | 0.4207 | 0.6127 | 0.4161 | 0.1689 | 0.2192 | 0.3874 | 0.6415 | 0.8007 | | 0.3323 | 35.0 | 2520 | 0.4402 | 0.4266 | 0.6148 | 0.4434 | 0.1728 | 0.2247 | 0.3754 | 0.6254 | 0.7983 | | 0.3374 | 36.0 | 2592 | 0.4336 | 0.4233 | 0.6139 | 0.4277 | 0.1706 | 0.2219 | 0.3810 | 0.6362 | 0.8008 | | 0.334 | 37.0 | 2664 | 0.4310 | 0.4230 | 0.6138 | 0.4240 | 0.1703 | 0.2209 | 0.3826 | 0.6345 | 0.8034 | | 0.3471 | 38.0 | 2736 | 0.4372 | 0.4250 | 0.6144 | 0.4397 | 0.1720 | 0.2240 | 0.3780 | 0.6303 | 0.8046 | | 0.3283 | 39.0 | 2808 | 0.4421 | 0.4301 | 0.6168 | 0.4497 | 0.1743 | 0.2259 | 0.3654 | 0.6209 | 0.7993 | | 0.3418 | 40.0 | 2880 | 0.4340 | 0.4224 | 0.6137 | 0.4334 | 0.1703 | 0.2228 | 0.3857 | 0.6351 | 0.8054 | | 0.3455 | 41.0 | 2952 | 0.4294 | 0.4174 | 0.6118 | 0.4212 | 0.1675 | 0.2203 | 0.3959 | 0.6469 | 0.8109 | | 0.3229 | 42.0 | 3024 | 0.4291 | 0.4165 | 0.6121 | 0.4199 | 0.1671 | 0.2207 | 0.4035 | 0.6464 | 0.8103 | | 0.352 | 43.0 | 3096 | 0.4393 | 0.4266 | 0.6154 | 0.4462 | 0.1729 | 0.2253 | 0.3744 | 0.6287 | 0.8049 | | 0.3163 | 44.0 | 3168 | 0.4250 | 0.4113 | 0.6098 | 0.4112 | 0.1647 | 0.2187 | 0.4041 | 0.6620 | 0.8201 | | 0.3284 | 45.0 | 3240 | 0.4358 | 0.4245 | 0.6138 | 0.4379 | 0.1716 | 0.2233 | 0.3745 | 0.6306 | 0.8106 | | 0.3359 | 46.0 | 3312 | 0.4321 | 0.4217 | 0.6124 | 0.4283 | 0.1699 | 0.2210 | 0.3770 | 0.6412 | 0.8129 | | 0.3406 | 47.0 | 3384 | 0.4238 | 0.4127 | 0.6104 | 0.4084 | 0.1653 | 0.2183 | 0.3982 | 0.6617 | 0.8177 | | 0.3207 | 48.0 | 3456 | 0.4375 | 0.4275 | 0.6147 | 0.4435 | 0.1733 | 0.2243 | 0.3658 | 0.6262 | 0.8071 | | 0.3338 | 49.0 | 3528 | 0.4331 | 0.4223 | 0.6142 | 0.4310 | 0.1705 | 0.2228 | 0.3846 | 0.6374 | 0.8071 | | 0.3203 | 50.0 | 3600 | 0.4308 | 0.4212 | 0.6136 | 0.4253 | 0.1695 | 0.2213 | 0.3878 | 0.6407 | 0.8054 | | 0.3238 | 51.0 | 3672 | 0.4379 | 0.4267 | 0.6148 | 0.4416 | 0.1727 | 0.2241 | 0.3723 | 0.6244 | 0.8036 | | 0.3209 | 52.0 | 3744 | 0.4289 | 0.4187 | 0.6121 | 0.4178 | 0.1681 | 0.2198 | 0.3920 | 0.6461 | 0.8096 | | 0.3198 | 53.0 | 3816 | 0.4376 | 0.4264 | 0.6145 | 0.4402 | 0.1724 | 0.2237 | 0.3708 | 0.6279 | 0.8066 | | 0.3137 | 54.0 | 3888 | 0.4294 | 0.4180 | 0.6115 | 0.4242 | 0.1681 | 0.2208 | 0.3888 | 0.6494 | 0.8152 | | 0.3238 | 55.0 | 3960 | 0.4416 | 0.4294 | 0.6158 | 0.4521 | 0.1743 | 0.2261 | 0.3645 | 0.6205 | 0.8069 | | 0.3173 | 56.0 | 4032 | 0.4257 | 0.4142 | 0.6116 | 0.4145 | 0.1661 | 0.2198 | 0.4016 | 0.6586 | 0.8136 | | 0.3173 | 57.0 | 4104 | 0.4303 | 0.4193 | 0.6123 | 0.4246 | 0.1687 | 0.2210 | 0.3879 | 0.6451 | 0.8118 | | 0.3297 | 58.0 | 4176 | 0.4302 | 0.4219 | 0.6132 | 0.4259 | 0.1700 | 0.2211 | 0.3792 | 0.6394 | 0.8122 | | 0.3261 | 59.0 | 4248 | 0.4319 | 0.4220 | 0.6131 | 0.4312 | 0.1702 | 0.2221 | 0.3781 | 0.6407 | 0.8142 | | 0.3082 | 60.0 | 4320 | 0.4340 | 0.4234 | 0.6136 | 0.4346 | 0.1710 | 0.2228 | 0.3754 | 0.6373 | 0.8106 | | 0.31 | 61.0 | 4392 | 0.4225 | 0.4120 | 0.6104 | 0.4073 | 0.1646 | 0.2181 | 0.4054 | 0.6626 | 0.8168 | | 0.3065 | 62.0 | 4464 | 0.4313 | 0.4197 | 0.6125 | 0.4280 | 0.1690 | 0.2216 | 0.3854 | 0.6472 | 0.8127 | | 0.3046 | 63.0 | 4536 | 0.4316 | 0.4202 | 0.6127 | 0.4268 | 0.1691 | 0.2213 | 0.3849 | 0.6448 | 0.8131 | | 0.303 | 64.0 | 4608 | 0.4352 | 0.4241 | 0.6137 | 0.4373 | 0.1712 | 0.2231 | 0.3760 | 0.6364 | 0.8097 | | 0.3094 | 65.0 | 4680 | 0.4318 | 0.4205 | 0.6128 | 0.4304 | 0.1695 | 0.2220 | 0.3828 | 0.6438 | 0.8140 | | 0.3035 | 66.0 | 4752 | 0.4351 | 0.4233 | 0.6136 | 0.4386 | 0.1709 | 0.2235 | 0.3781 | 0.6388 | 0.8099 | | 0.327 | 67.0 | 4824 | 0.4307 | 0.4203 | 0.6131 | 0.4280 | 0.1693 | 0.2216 | 0.3828 | 0.6463 | 0.8143 | | 0.3175 | 68.0 | 4896 | 0.4325 | 0.4219 | 0.6137 | 0.4314 | 0.1701 | 0.2222 | 0.3809 | 0.6406 | 0.8135 | | 0.3188 | 69.0 | 4968 | 0.4299 | 0.4203 | 0.6126 | 0.4271 | 0.1694 | 0.2214 | 0.3827 | 0.6440 | 0.8141 | | 0.3158 | 70.0 | 5040 | 0.4304 | 0.4203 | 0.6126 | 0.4274 | 0.1694 | 0.2215 | 0.3832 | 0.6443 | 0.8133 | | 0.3298 | 71.0 | 5112 | 0.4315 | 0.4219 | 0.6135 | 0.4292 | 0.1700 | 0.2218 | 0.3792 | 0.6423 | 0.8136 | | 0.3246 | 72.0 | 5184 | 0.4323 | 0.4219 | 0.6129 | 0.4322 | 0.1703 | 0.2223 | 0.3769 | 0.6418 | 0.8133 | | 0.3116 | 73.0 | 5256 | 0.4301 | 0.4198 | 0.6124 | 0.4264 | 0.1691 | 0.2213 | 0.3833 | 0.6459 | 0.8141 | | 0.3192 | 74.0 | 5328 | 0.4301 | 0.4200 | 0.6125 | 0.4266 | 0.1691 | 0.2213 | 0.3819 | 0.6464 | 0.8156 | | 0.3172 | 75.0 | 5400 | 0.4305 | 0.4203 | 0.6123 | 0.4280 | 0.1694 | 0.2214 | 0.3813 | 0.6446 | 0.8152 | | a8f17a03923eabd0963c6687d30f4cff |
mit | [] | false | Dragonborn on Stable Diffusion This is the `<dragonborn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:        | 525fe8138f74e200ba36d25f3d72b15f |
apache-2.0 | ['generated_from_keras_callback'] | false | tf_bert_uncased_emotion_detection This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0659 - Train Accuracy: 0.9661 - Validation Loss: 0.1150 - Validation Accuracy: 0.9370 - Epoch: 9 | 88b4e441f59e0d105eb319b8a4a31efc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 9fdf746970d5cd359f6ebdfa7a32466f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3703 | 0.8683 | 0.1511 | 0.9315 | 0 | | 0.1208 | 0.9414 | 0.1145 | 0.9380 | 1 | | 0.0820 | 0.9561 | 0.1150 | 0.9370 | 2 | | 0.0656 | 0.9681 | 0.1150 | 0.9370 | 3 | | 0.0643 | 0.9671 | 0.1150 | 0.9370 | 4 | | 0.0652 | 0.9697 | 0.1150 | 0.9370 | 5 | | 0.0646 | 0.9689 | 0.1150 | 0.9370 | 6 | | 0.0651 | 0.9678 | 0.1150 | 0.9370 | 7 | | 0.0651 | 0.9691 | 0.1150 | 0.9370 | 8 | | 0.0659 | 0.9661 | 0.1150 | 0.9370 | 9 | | e1662cd5de3be4800313567f7eb926cc |
apache-2.0 | ['generated_from_trainer'] | false | function-arg-swap-model-148k-files-365k-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4783 - Accuracy: 0.7679 - Precision: 0.7641 - Recall: 0.7812 - F1 score: 0.7725 | fd225a788eedc8fb1018dbe7e9378931 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 105a8dfe8c796f4c0e5c7dce8467715f |
cc-by-sa-3.0 | ['spacy'] | false | xx_sent_ud_sm Multi-language pipeline optimized for CPU. Components: senter. | Feature | Description | | --- | --- | | **Name** | `xx_sent_ud_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `senter` | | **Components** | `senter` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.8 (UD_Afrikaans-AfriBooms, UD_Croatian-SET, UD_Czech-CAC, UD_Czech-CLTT, UD_Danish-DDT, UD_Dutch-Alpino, UD_Dutch-LassySmall, UD_English-EWT, UD_Finnish-FTB, UD_Finnish-TDT, UD_French-GSD, UD_French-Spoken, UD_German-GSD, UD_Indonesian-GSD, UD_Irish-IDT, UD_Italian-TWITTIRO, UD_Korean-GSD, UD_Korean-Kaist, UD_Latvian-LVTB, UD_Lithuanian-ALKSNIS, UD_Lithuanian-HSE, UD_Marathi-UFAL, UD_Norwegian-Bokmaal, UD_Norwegian-Nynorsk, UD_Norwegian-NynorskLIA, UD_Persian-Seraji, UD_Portuguese-Bosque, UD_Portuguese-GSD, UD_Romanian-Nonstandard, UD_Romanian-RRT, UD_Russian-GSD, UD_Russian-Taiga, UD_Serbian-SET, UD_Slovak-SNK, UD_Spanish-GSD, UD_Swedish-Talbanken, UD_Telugu-MTG, UD_Vietnamese-VTB)](https://universaldependencies.org/) (Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell; et al.) | | **License** | `CC BY-SA 3.0` | | **Author** | [Explosion](https://explosion.ai) | | ab6c6e1c4fb91a6131eb8486e128a651 |
apache-2.0 | [] | false | Model Description FLIPPED uses a unique meta-learning method to show zero-shot task generalization on classification natural language prompts, outperforming GPT-3 and T0-11B on many tasks with a 4x smaller scale. It is a series of encoder-decoder model trained on a numerous classification dataset. We show inputs and its corresponding outputs of each instances in each dataset to FLIPPED, and train it to generate its possible instruction. We add unlikelihood loss in order **not** to generate the instruction when given the same input, but a wrong output. To obtain FLIPPED, we fine-tune a T5 model in a given scale on a multitask mixture covering many different classification NLP tasks. | 0e5f405664f2e719e766432ba970ce78 |
apache-2.0 | [] | false | Intended uses You can use the models to perform inference on tasks by specifying your input-output NLP query in a "input: {input}\noutput: {output}" form , and the model will predict the instruction. For example, You can try *"input: <extra_id_0> this is the best cast iron skillet you will ever buy<extra_id_1>\noutput: Positive"* as an input, and the model will hopefully generate *"Title: Review:"*. | d20a0f6634d9114f2d2eb4e140a3676a |
apache-2.0 | [] | false | How to use Our overall explanation models along with ablations can be found in our [paper](https://arxiv.org/abs/2210.02969). We recommend using the [FLIPPED-11B](seonghyeonye/flipped_11B) checkpoint as it leads (on average) to the best performances on a variety of NLP tasks. |Model|Number of parameters| |-|-| |[Flipped_11B](https://huggingface.co/seonghyeonye/flipped_11B)|11 billion| |[Flipped_3B](https://huggingface.co/seonghyeonye/flipped_3B)|3 billion| Here is how to download the model in PyTorch: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("seonghyeonye/flipped_11B") tokenizer = T5Tokenizer.from_pretrained("seonghyeonye/flipped_11B") ``` If you want to use another checkpoint, please replace the path in `T5Tokenizer` and `T5ForConditionalGeneration`. We also provide a quick [Jupyter Notebook](https://github.com/seonghyeonye/Flipped-Learning/blob/master/flipped_inference.ipynb) where you can inference with our method. **Note: the model was trained with bfloat16 activations. As such, we highly discourage running inference with fp16.** | 1e5130755d5bc84101ae4cc1e3c0d31b |
apache-2.0 | [] | false | Training procedure FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-xxl), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). At a high level, the input text along with output label is fed to the encoder and the instruction text is produced by the decoder. The model is fine-tuned to autoregressively generate the target. We also feed input text along with a wrong input, adding an unlikelihood loss in order not to make model produce the proper instruction in that case. Here are our training details. Training details: - Fine-tuning steps: 5'000 - Input sequence length: 384 - Target sequence length: 64 - Batch size: 240 - Optimizer: Adafactor - Learning rate: 5e-5 - Dropout: 0.1 - Sampling strategy: proportional to the number of examples in each dataset (we randomly sampled any dataset if it has over 500'000 examples so that it has at most 500'000 examples. Also, we randomly choose which instruction to generate for each training steps, so ideally each instruction appears *num_examples/num_templates* while training.) | 93f125a3e6515db51772329bf612983b |
apache-2.0 | [] | false | Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |FLIPPED-11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Topic Classification: AG News, DBPedia<br>- Paraphrase Identification: MRPC, PAWS, QQP| |FLIPPED_3B|Same as FLIPPED-11B| We only choose prompts examples that has output lables, which can be found on the dataset page. | 9c70884910692cec06617308a2b1fa55 |
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