license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['Twitter', 'Multilingual'] | false | Citation If you use TwHIN-BERT or out datasets in your work, please cite the following: ```bib @article{zhang2022twhin, title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations}, author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams,... | faa8a5f55dbdc2cd4affdc895c99606c |
apache-2.0 | ['roberta-wwm'] | false | 使用Huggingface-Transformers 依托于[Huggingface-Transformers](https://github.com/huggingface/transformers)，可轻松调用以上模型。 ``` tokenizer = BertTokenizer.from_pretrained("MODEL_NAME") model = BertModel.from_pretrained("MODEL_NAME") ``` **注意：本目录中的所有模型均使用BertTokenizer以及BertModel加载，请勿使用RobertaTokenizer/RobertaModel！** 其中`MODEL_NAME... | 8b2eff3b5ec315500be38085246af16e |
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 | 282 | 3.3270 | 17.3937 | 4.0098 | 13.0087 | 15.3801 | 18.98... | 4e04de5c9f7aa7a38b6400bbdd0be5b6 |
mit | ['spacy', 'token-classification'] | false | uk_core_news_md Ukrainian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `uk_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morpholog... | 0872e5c78a6ccd522f92859352b54849 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1211 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=CCONJ`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=P... | f2e43c3b09ca479ecabdbbb82a7cbe6c |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.99 | | `TOKEN_P` | 99.99 | | `TOKEN_R` | 99.97 | | `TOKEN_F` | 99.98 | | `POS_ACC` | 98.19 | | `MORPH_ACC` | 95.19 | | `MORPH_MICRO_P` | 97.85 | | `MORPH_MICRO_R` | 97.15 | | `MORPH_MICRO_F` | 97.50 | | `SENTS_P` | 94.18 | | `SENTS_R` | 90.60 | | `SENTS_F` | ... | 4de7b8a92425fe3ad7332f74bea4a1a4 |
mit | ['question-generation'] | false | T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with `<hl>` t... | b07d1f1a4d6b1db4f6dd9e0135ed3f48 |
apache-2.0 | ['generated_from_trainer'] | false | try_connll-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: 0.0596 - Precision: 0.9283 - Recall: 0.9372 - F1: 0.9328 - Accuracy: 0.9841 | 4f301ecaf236b940098aca17fb2e4d22 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2383 | 1.0 | 878 | 0.0691 | 0.9139 | 0.9239 | 0.9189 | 0.9810 | | 0.0497 | 2.0 |... | f914c4cc2a364cf8c7fd84c9f973289a |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | mt5-small-finetuned-arxiv-cs This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on a subset of the arxiv dataset. It achieves the following results on the evaluation set: - Loss: 1.6922 - Rouge1: 0.7734 - Rouge2: 0.2865 - Rougel: 0.6665 - Rougelsum: 0.6743 | 08b50d74ce896a7d2475d483802288e6 |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 14.0947 | 1.0 | 500 | 2.7666 | 1.2101 | 0.459 | 1.1426 | 1.1385 | | 2.8524 | 2.0 | 1000 ... | 7ad434adc1ec3e256770068c1072d2c5 |
apache-2.0 | ['StableDiffusion', 'Warhammer', 'wh40k'] | false | StableDiffusion model trained on Sororitas Sisters of Battle dataset Use token whsororitas for Sororitas Use token whinsignia for Insignia-themed items - Samples      on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1358 - F1: 0.8638 | a5fa8b3c7d8daef1c493cb26f81c2eb8 |
mit | ['generated_from_trainer'] | false | farsi_lastname_classifier_2 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0370 - Pearson: 0.9361 | 210068d646baf28b3824dd55cc5ea2de |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.0 | 12 | 0.2937 | 0.7153 | | No log | 2.0 | 24 | 0.1063 | 0.8056 | | No log | 3.0 | 36 | 0.0530 | 0.9110... | 743765178b450ce1b32f498b27db72fd |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on titanic_traink4m62li8 to apply classification on survived **Metrics of the best model:** accuracy 0.975294 average_precision 0.983664 roc_auc 0.987422 recall_macro 0.971786 f1_macro 0.973370 Name: MultinomialNB(), dtype: float64 **See ... | ebb4e53c666fd7587190425a5bfb6981 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float ... free_string useless passenger_id True False ... False False pclass False False ... False False name False False ... True False sex False ... | 7571c7486007c98ef7fdcb02378d3e64 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">... | a8806286ba5a996d63956c7bc02784a4 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | bart-base-finetuned-samsum-v2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5326 - Rouge1: 47.3928 - Rouge2: 24.0713 - Rougel: 40.029 - Rougelsum: 43.6252 - Gen Len: 17.815... | 25e421d5f709be9d64fcf09ce6d1da86 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | be2946bd81aa611daa7293d453b01792 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 1.59 | 1.0 | 1841 | 1.5326 | 47.3928 | 24.0713 | 40.029 | 43.6252 | 17.81... | e4e322c928ab591a6f82579961132232 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_stsb_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1255 - Pearson: nan - Spearmanr: nan - Combined Score: nan | 047ac08d3a787f15b0d370683f6a5376 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 4.2655 | 1.0 | 23 | 3.2719 | 0.0074 | 0.0048 | 0.0061 | | 3.8876 | 2.0 | 46 ... | 7ab01239a8b03e836761d5bc10afb99b |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-finetuned-xsum-v3 This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3377 - Rouge1: 99.9461 - Rouge2: 72.6619 - Rougel: 99.9461 - Rougelsum: 99.9... | 3fb701702bd83d6145f001687c8ae045 |
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 | 139 | 0.3653 | 96.4972 | 70.8271 | 96.5252 | 96.5085 | 9.... | 24514f77585562245822f9e4e500d687 |
apache-2.0 | ['generated_from_trainer'] | false | nyaszzzz This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.4490 | 066760623fe8fe208f1e4712cffd2428 |
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: 0.5 | 3a8abd5dc53faf45786898142df0af97 |
mit | ['timelms', 'twitter'] | false | Twitter June 2022 (RoBERTa-base, 154M) This is a RoBERTa-base model trained on 153.86M tweets until the end of June 2022 (15M tweets increment). More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829). Below, we provide some usage examples using the standard Transfo... | e998e6b5784f72bd6fd8d46f26bba5e0 |
mit | ['timelms', 'twitter'] | false | Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): ... | c7ccf2871e41477e3ec46ba966121547 |
mit | ['timelms', 'twitter'] | false | naive approach for demonstration text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() return np.mean(features[0], axis=0) MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr" tokenizer = AutoToke... | f844a5dcf0934772e4b19b288ad38172 |
mit | ['timelms', 'twitter'] | false | Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) | 248d8ad9ce04be4095e1edac0e73ed51 |
apache-2.0 | ['translation'] | false | opus-mt-gil-es * source languages: gil * target languages: es * OPUS readme: [gil-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gil-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 0121184aba41a8921a0f99751b759661 |
mit | ['generated_from_trainer'] | false | 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 | e4d3b996ed9dac0f0044363df8fbe334 |
mit | ['generated_from_trainer'] | false | 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.... | 805158bceeee3c21b30f648012ff1095 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_wav2vec2_s727 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 9ee90e349823e4f27f1b8b23bb574e1f |
apache-2.0 | ['generated_from_keras_callback'] | false | Lunage/my_distilbert-finetuned-imdb 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: - Train Loss: 3.6915 - Validation Loss: 3.4024 - Epoch: 0 | ca89e9ac9b102fb5162f45a14c927279 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | a89056e40fe9970928a9f2503c265d63 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) * language: el * licence: apache-2.0 * dataset: ~23.4 GB of Greek corpora * model: GPT2 (12-layer, 768-hidden, 12-heads, 117M parameters. OpenAI GPT-2 English model, finetuned for the Greek language) * pre-processing: tokenization + BPE s... | 5a9377ec3d33848aaaf425f7f52069b7 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Model description A text generation (autoregressive) model, using Huggingface transformers and fastai based on the English GPT-2. Finetuned with gradual layer unfreezing. This is a more efficient and sustainable alternative compared to training from scratch, especially for low-resource languages. Based on the wor... | 4085d93fc16a03f32dadfe562eddb6bc |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use ``` from transformers import pipeline model = "lighteternal/gpt2-finetuned-greek" generator = pipeline( 'text-generation', device=0, model=f'{model}', tokenizer=f'{model}') text = "Μια φορά κι έναν καιρό" print("\ ".join([x.get("generated_text") for x in generator( text, max... | 2eb9d7d63be33635faf44368c417ae37 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Training data We used a 23.4GB sample from a consolidated Greek corpus from CC100, Wikimatrix, Tatoeba, Books, SETIMES and GlobalVoices containing long senquences. This is a better version of our GPT-2 small model (https://huggingface.co/lighteternal/gpt2-finetuned-greek-small) | 0c951cceaed36c70185a94ed41d2d56a |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgement The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call) Based on the work of Thomas Dehaene (ML6): https://blog.ml6.eu/dutch-gpt2-autoregressive-language-modelling-on-a-budget-cff3942dd020 | d762c06e0ff7f8f20796dfc2692b2314 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-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: 0.4779 - Wer: 0.3468 | dbbd97276ada1825001ed4d32c531849 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4408 | 4.0 | 500 | 1.2302 | 0.9116 | | 0.561 | 8.0 | 1000 | 0.4809 | 0.4320 | | 0.2091 | 12.0 | 1500 | 0.4285 | 0.3880 | |... | d1577e4b7d8619d44d9238848d24696c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-transformers-github-128 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: 1.2348 | d8e3779825db85353437050ee0106516 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0247 | 1.0 | 582 | 1.6457 | | 1.5989 | 2.0 | 1164 | 1.4157 | | 1.4449 | 3.0 | 1746 | 1.3494 | | 1.3579 | 4.0 | 2328 | 1.3774 ... | 31ba48e2c5ff9cddd8761f2d0c1c1e87 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-nya This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5086 - Wer: 27.5487 | 510420ab9ffaa7e546251dafd5b82caf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 49472349f7efdf129b3b694b210447a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2671 | 0.99 | 500 | 0.5633 | 35.9244 | | 0.1372 | 1.97 | 1000 | 0.4515 | 48.1630 | | 0.0742 | 2.96 | 1500 | 0.4474 | 32.498... | 5cadfc648261658a2f60dedb3d94dc05 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-zeroth This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.7052 - Wer: 0.4621 | e15fba4c10f431ef918b3cb1d8c1e1b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 15.1763 | 1.61 | 400 | 4.6768 | 1.0 | | 3.1779 | 3.21 | 800 | 1.6680 | 0.8752 | | 1.052 | 4.82 | 1200 | 0.9580 | 0.7332 | |... | e8b06ce636ac9665159ad0765669ccd5 |
apache-2.0 | ['translation'] | false | opus-mt-tw-sv * source languages: tw * target languages: sv * OPUS readme: [tw-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tw-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | bbb2b945b65aed1f60a3e5a89cec2371 |
mit | ['summarization', 'generated_from_trainer'] | false | bart-base-cnn-xsum-wiki-swe This model is a fine-tuned version of [Gabriel/bart-base-cnn-xsum-swe](https://huggingface.co/Gabriel/bart-base-cnn-xsum-swe) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3884 - Rouge1: 26.8917 - Rouge2: 11.8254 - Rougel: 22.6089 - Rougelsum: 26.1... | 5c7421443cf2fcc18a8c690c3578b84c |
mit | ['summarization', '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 - 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_sch... | 842b921b82614a3d9d322196018425bd |
mit | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.4993 | 1.0 | 2985 | 2.3834 | 25.8959 | 10.9373 | 21.8329 | 25.2002 |... | e9e23d0b657a563fcd2193c74a0c5a21 |
apache-2.0 | ['generated_from_keras_callback'] | false | distil-bert-finetuned-log-parser-winlogbeat This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: | 74f6cfc33d7f41c6d3d26e58a5cac9b9 |
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': 2e-05, 'decay_steps': 1635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 0a62107be6429c66162c593c1b0de014 |
apache-2.0 | ['generated_from_trainer'] | false | 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_sch... | 79fc5e02fc704ff04d08d29e1a9d769f |
apache-2.0 | ['translation'] | false | opus-mt-crs-en * source languages: crs * target languages: en * OPUS readme: [crs-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/crs-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](http... | 200df24f78668e65e42024dbdf814ab3 |
creativeml-openrail-m | ['text-to-image', 'v2.1', 'Embedding'] | false | TI embedding trained on 768x768 stills from 'The Transformers. The Movie' (1986). *Install by downloading the embedding, and putting it in the **\embeddings** folder.* *Use embedding's filename in your prompt to activate the style*  on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5282 - Wer: 0.3302 | 4b825e2f4a3d9b900e3ff046e11e29a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5185 | 1.0 | 500 | 1.5798 | 0.9593 | | 0.8096 | 2.01 | 1000 | 0.5024 | 0.5082 | | 0.4196 | 3.01 | 1500 | 0.4594 | 0.448... | 942f3fa38cf64232514a7fd0f13db10b |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | Pretrained Model Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz. **Note: The result from th... | 0d52ebcf5520eefc38a915bd37b74886 |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | Training Script Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation). In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/tamil-finetun... | f8818f2a937b454c2ff962fba20ed54c |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): | a3da51b4b8b899bdf6fc8bd33f3aa53a |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | load pretrained model processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250") | 26706c2cd5c4d6b8c4c321ed33025b80 |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | Evaluation The model can be evaluated as follows on the hindi 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", "ta", split="test") wer =... | 82225d2b6cd13c3e5c34a3dd3ca06bb0 |
mit | ['audio', 'automatic-speech-recognition', 'speech'] | false | 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")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] =... | 8c285bb5a43963297c31b7442f978cda |
mit | ['bridgetower'] | false | BridgeTower large-itm-mlm model The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The model was pretrained on English language using masked language modeling ... | 9a0f2f6eb0cc455ec6a24c1869a43b08 |
mit | ['bridgetower'] | false | How to use Here is how to use this model to perform image and text matching: ```python from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval import requests from PIL import Image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=T... | 4863869ae62b2c35e4d883368d314fd1 |
mit | ['bridgetower'] | false | prepare inputs encoding = processor(image, text, return_tensors="pt") outputs = model(**encoding) scores[text] = outputs.logits[0,1].item() ``` Here is how to use this model to perform masked language modeling: ```python from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM from PIL impor... | efe1753321a497dde5a710b31943c8e8 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab10 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: 0.4460 - Wer: 0.3425 | 7213f369dcb4b8ada1b23f25cc09b313 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.9891 | 3.52 | 500 | 3.1554 | 1.0 | | 1.71 | 7.04 | 1000 | 0.7122 | 0.5811 | | 0.6164 | 10.56 | 1500 | 0.5149 | 0.4880 | |... | 5bf55edce40fc5b60c585056bc7303ea |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-1000-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4398 - Accuracy: 0.2308 ... | 46ce137a579c9d65addec16032ecd833 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0631 | 1.0 | 1 | 4.8365 | 0.1509 | | 4.2899 | 2.0 | 2 | 4.6738 | 0.2041 | | 3.7697 | 3.0 | 3 | 4.5378 | 0.... | c20f9a48bc1f8b4ed7f523cf9ada5704 |
creativeml-openrail-m | ['stable diffusion', 'stable diffusion diffusers', 'SlimeX'] | false | **[SlimeX](https://civitai.com/models/6963/slimex) by [Zanc](https://civitai.com/user/Zanc) (owner)** **This model intends to produce high-quality, highly detailed anime style SFW and NSFW images.** - **Slime** = No vae - **SlimeX** = vae included | ddc52285e007145b2d9a023ace311ae3 |
creativeml-openrail-m | ['stable diffusion', 'stable diffusion diffusers', 'SlimeX'] | false | 1  ``` masterpiece, best quality, 1girl, beautiful detailed eyes, perfect face, beautiful detailed face, looking at viewer, sigma 400mm f1.8, photo fine print, amazing sharp focus, ultra detailed, si... | 794f1b1f01e12a4bcac3d313ce9e256f |
creativeml-openrail-m | ['stable diffusion', 'stable diffusion diffusers', 'SlimeX'] | false | 2  ``` masterpiece, best quality, izekonabe akio, 1girl Negative prompt: (worst quality, low quality:1.4), (monochrome:1.4) Size: 448x576, Seed: 3548745218, Model: SlimeX, Steps: 20, Sampler: DDIM, C... | 79ca83abe772affad2a18f9907432fbc |
creativeml-openrail-m | ['stable diffusion', 'stable diffusion diffusers', 'SlimeX'] | false | 3  ``` masterpiece, best quality, ilyotaka haruhiko, solo, 1girl, solo, hair between eyes, long hair, short beard, light white purple hair, short hair, medium breasts, looking at viewer, thigh highs ... | 2621b3da4ba75cb80edba915d6bec8e8 |
apache-2.0 | [] | false | Model description Skein is a series of hybrid story generation models intended for use in both text adventure writing and normal novel-style writing. The models are known to possess a strong second person bias. For inquiries, please contact the KoboldAI community. The name comes from the Integrated Development Enviro... | 8250d82f2da946e5e3cd433b3922b4d6 |
apache-2.0 | [] | false | Training procedure GPT-NeoX-20B-Skein was trained on a TPUv3-32 TPU pod using a heavily modified version of Ben Wang's Mesh Transformer JAX library, the original version of which was used by EleutherAI to train their GPT-J-6B model. The training hyperparameters and statistics can be found [here](https://wandb.ai/ve-fo... | 9dcf0a248304dffcdbcc9e2a42d80c35 |
apache-2.0 | [] | false | Training data The data are mostly comprised of light novels from the dataset of the [KoboldAI/GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) model and assorted interactive fiction. The dataset uses `[Themes: <comma-separated list of genres>]` for tagging. For more details, consult [this ... | 50e1be38c9e266ebfca48782c25de227 |
apache-2.0 | [] | false | Citation details The GPT-NeoX-20B model weights: ```bibtex @inproceedings{gpt-neox-20b, title={{GPT-NeoX-20B}: An Open-Source Autoregressive Language Model}, author={Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDon... | 3709e5126cd0e76823f9a97cedbbfc67 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-FER2013-7e-05 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder da... | a4da4c197e0b166401dcad9c582ceadd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | c796eb9ea9de35158ed6dc970a710430 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4564 | 1.0 | 224 | 0.9463 | 0.7014 | | 0.6463 | 2.0 | 448 | 0.9121 | 0.7116 | | 8a83f57838bd5a8289d1a5ad3bf803c3 |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization', 'conversational'] | false | MVP The MVP model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIB... | de2109d8df6c25cb70b9f106e702e45c |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization', 'conversational'] | false | Model Description MVP is supervised pre-trained using a mixture of labeled datasets. It follows a standard Transformer encoder-decoder architecture. MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-tex... | 5863ab9dc3baf3d3b75f98c0f4f4e7eb |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization', 'conversational'] | false | Examples For summarization: ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp") >>> inputs = tokenizer( ... "Summarize: You may want to stick it to you... | 8b6d4abf3757952fa97833539957f200 |
mit | ['generated_from_trainer'] | false | pretrained_model This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the go_emotions dataset. It achieves the following results on the evaluation set: - Loss: 0.0568 - F1: 0.5868 - Roc Auc: 0.7616 - Accuracy: 0.4821 | debe62f939a5815b57bfda3da22e956a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.1205 | 1.0 | 679 | 0.0865 | 0.5632 | 0.7347 | 0.4458 | | 0.0859 | 2.0 | 1358 | 0.0829 | 0.5717 ... | e4080dd457dc66c01194b4d879b71739 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2-with-unfamiliar-words 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: 0.0870 - Accuracy: 0.9866 | 835498389f341cf2f18b6d927a2337c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2917 | 1.0 | 975 | 0.0703 | 0.9778 | | 0.063 | 2.0 | 1950 | 0.0815 | 0.9821 | | 0.0233 | 3.0 | 2925 | 0.0680 | 0.... | 26b09a005b7ede1de7752879a65ba92d |
apache-2.0 | ['image-classification', 'vision'] | false | Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transfor... | d49f5ade89402359268ee15a2b572f91 |
apache-2.0 | ['image-classification', 'vision'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 mill... | bf0cdde61816e39cd42378c4e1ac4938 |
apache-2.0 | ['image-classification', 'vision'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image =... | 636f38df3105be52d7a0e0bf3302c76b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.372e-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 3138344630 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 100 - mixed_pr... | 160da65a202210567ed082d99f52cce9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1261 | 13.0 | 8619 | 3.4600 | | 1.141 | 14.0 | 9282 | 3.4634 | | 1.1278 | 15.0 | 9945 | 3.4665 | | 1.1183 | 16.0 | 10608 | 3.4697 ... | a628015c977e04fa596757236208baff |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5955 | 1.0 | 25 | 1.4376 | | 1.4736 | 2.0 | 50 | 1.2969 | | 1.3925 | 3.0 | 75 | 1.3163 | | abf2c1bd2984de32fa50d015c7e97e8a |
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