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 | ['generated_from_keras_callback'] | false | robbery_dataset_tf_finetuned_20221113 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0506 - Train Sparse Categorical Accuracy: 0.9844 -... | 93609545b902417a024fbed727d6a3a5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.4908 | 0.8335 | 0.2872 ... | 2a36d444ae1f815e88b3f1c557d28f2e |
apache-2.0 | ['speech', 'automatic-speech-recognition'] | false | Wav2Vec2-Base-Pretrain-Vietnamese The base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled data in [VLSP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing). When using the model make sure that your speech input is also sampled at 16Khz. Note ... | 925f0200029552d61165575c50708ad9 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_160k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 160k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 743ed048c81dda07e3cd00634aa094af |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_160k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_160k') model = TFBertModel.from_pretrained("google/multibe... | 3c9a9af29a79deb9d4677f998671c6ea |
mit | ['normalization', 'denoising autoencoder', 'russian'] | false | This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences. This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrated/rut5-small) model on the task of reconstructing a sentence: * restoring word positions (after slightly shuffling them) * restoring... | 8ec7137003969247970feec56487eb33 |
mit | ['normalization', 'denoising autoencoder', 'russian'] | false | !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small-normalizer") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small-normalizer") text = 'меня тобой не понимать' i... | 84beff72be96dc8b485f8a7ff79b3584 |
apache-2.0 | ['generated_from_trainer'] | false | thucnews This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the load_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3191 - Accuracy: 0.9433 | 0c976c48330e5903a0b4b650ce5437c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP | ac487281a8339b58d1a3dbb2f41bd37c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2038 | 1.0 | 704 | 0.2018 | 0.9332 | | 0.1403 | 2.0 | 1408 | 0.1829 | 0.9406 | | 0.0894 | 3.0 | 2112 | 0.2073 | 0.... | 2347c251763b4166f5b28f0799970d64 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab92 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: - eval_loss: 0.6596 - eval_wer: 0.4164 - eval_runtime: 55.6472 - eval_samples_per_second: 12.615 ... | fd7b61f93b340921685cd76f163edc8f |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper6_mesum5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set: - Loss: 0.8241 - Accuracy: 0.8036 | d942b57c34c68c68ef51cc304cdd90e4 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 16 - mixed_precision_training: Native AMP | 3e0112f92f61e63ae1aefc4d36b55fb4 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.9276 | 0.23 | 100 | 3.8550 | 0.2089 | | 3.0853 | 0.47 | 200 | 3.1106 | 0.3414 | | 2.604 | 0.7 | 300 | 2.5732 | 0.... | f2b70d728bf8ea12c8b4dec1f6d6b605 |
apache-2.0 | ['es', 'en', 'codemix'] | false | BERT codemixed base model for Hinglish (cased) This model was built using [lingualytics](https://github.com/lingualytics/py-lingualytics), an open-source library that supports code-mixed analytics. | d19bc837f19fd9ab51f5afc65b7f4e87 |
apache-2.0 | ['es', 'en', 'codemix'] | false | Model description Input for the model: Any codemixed Hinglish text Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive) I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [SAIL 2017](http://www.dasdipankar.com/SAILCodeMixed.html) dataset. | 5172fd350865d9bf129186688edc9c69 |
apache-2.0 | ['es', 'en', 'codemix'] | false | Eval results Performance of this model on the dataset | metric | score | |------------|----------| | acc | 0.55873 | | f1 | 0.558369 | | acc_and_f1 | 0.558549 | | precision | 0.558075 | | recall | 0.55873 | | 12064d3589d6d62ead760657d65f3cb4 |
apache-2.0 | ['es', 'en', 'codemix'] | false | You can include sample code which will be formatted from transformers import BertTokenizer, BertModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased') model = AutoModelForSequenceClassification.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased') t... | 55f2ad77bc0abd6445344d02f86bd828 |
apache-2.0 | ['es', 'en', 'codemix'] | false | Preprocessing Followed standard preprocessing techniques: - removed digits - removed punctuation - removed stopwords - removed excess whitespace Here's the snippet ```python from pathlib import Path import pandas as pd from lingualytics.preprocessing import remove_lessthan, remove_punctuation, remove_stopwords from ... | 3c8e7acfe4f86f7bbb3a7ed0efdb2d5a |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-squadshifts-vanilla-amazon-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/asahi... | b88ebb352a143a56520ccda60aac95f8 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (amazon) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-quest... | 4bccb999788675a7f2cd09055f3bc2e5 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-squadshif... | 6ed1c41e46c73c938ff9de0744c5f4cd |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-squadshifts-vanilla-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | Score | Type | Dataset ... | b7d3409142c714a1f9fa298f602f8549 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: amazon - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 7 - batc... | 33d88c3586299da284002e24a109143b |
creativeml-openrail-m | [] | false | Artist 1: WLOP\ Patreon: https://www.patreon.com/wlop/posts Artist 2: Nixeu\ Patreon: https://www.patreon.com/nixeu/posts Artist 3: Cutesexyrobutts\ Patreon: https://www.patreon.com/cutesexyrobutts | 2634eaa48c240fb4caa2a6854bd39d60 |
creativeml-openrail-m | [] | false | Basic explanation Token words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, bu... | 4344b3626c8e14ba341adb9341ce1750 |
apache-2.0 | ['generated_from_trainer'] | false | lab9_model_bert This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6498 | 180ef796159441e91d0d4be3f40ab3e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 50 | 3.6137 | | No log | 2.0 | 100 | 1.9421 | | No log | 3.0 | 150 | 1.2792 | | No log | 4.0 | 200 | 1.0015 ... | 314bf1de8131a2db03822cfbf56b7bc5 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 384, 'force_call_on': [12588], 'metrics_con... | e4ba619fafa061fcb347a35e1291b3a6 |
apache-2.0 | ['translation'] | false | ita-msa * source group: Italian * target group: Malay (macrolanguage) * OPUS readme: [ita-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-msa/README.md) * model: transformer-align * source language(s): ita * target language(s): ind zsm_Latn * model: transformer-align * pre-processin... | 162ef1ad999dd167cbdfd2eee1f3d5f6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ita-msa - source_languages: ita - target_languages: msa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-msa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ms'] - src_constituents: {'ita'} - tgt_const... | 517fb09961cfa2d165c7e82e97b4cc12 |
apache-2.0 | [] | false | BERT large model (uncased) whole word masking finetuned on SQuAD Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is un... | d6dbbe2e6068112e17889589415c4439 |
apache-2.0 | [] | false | Intended uses & limitations This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html | 2f0d581e546092bd847686400538fd8b |
apache-2.0 | [] | false | Fine-tuning After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. In order to reproduce the training, you may use the following command: ``` python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_qa.py \ --model_name_or_path bert-l... | 3a84ebd5f2f705a851ebee68194c9851 |
apache-2.0 | ['translation'] | false | opus-mt-sv-sm * source languages: sv * target languages: sm * OPUS readme: [sv-sm](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-sm/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | b3e9bfa340e142f1d910a560b742f8bb |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_500v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2645 - Precision: 0.7018 - Recall: 0.6812 - F1: 0.6913 - Accura... | f46f97ab67c80fe534460605ef8b2276 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 171 | 0.2364 | 0.6168 | 0.5804 | 0.5980 | 0.9178 | | No log | 2.0 |... | 70a764cbd8c547256a7dcfad4edf3ee3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-large-uk This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2527 - eval_wer: 10.0226 - eval_runtime: 9610.7996 - eval_samples_per_second: 0.747... | 92eef1a79e63e5e4e4d535f1046a72e2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - traini... | cc66fe6639ffe952b2cdc6bafc4ddbcf |
creativeml-openrail-m | ['text-to-image'] | false | 2D Illustration Styles are scarce on Stable Diffusion. Inspired by Hollie Mengert, this a fine-tuned Stable Diffusion model trained on her work. The correct token is holliemengert artstyle. Hollie is **not** affiliated with this. You can read about her stance on the issue here - https://waxy.org/2022/11/invasive-diffu... | 7262acb31ddb5e66896868b360261611 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilbert-i 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: 1.0288 | a06498b3329594c6664b8fdeed316b41 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3931 | 1.0 | 152 | 1.7738 | | 1.7533 | 2.0 | 304 | 1.5109 | | 1.5584 | 3.0 | 456 | 1.4003 | | 1.443 | 4.0 | 608 | 1.3296 ... | 5c349a12e0f879409baf2135272ec30e |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_qqp This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5008 - Accuracy: 0.7600 - F1: 0.6402 - Combined Score: 0.7001 | a3fafd7dcb700a9746d1609e1727a4b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.6505 | 1.0 | 2843 | 0.6498 | 0.6321 | 0.0012 | 0.3166 | | 0.6474 | 2.0 | 5686 | ... | cb6138f984fe6853584cc513ca57cd9b |
apache-2.0 | ['generated_from_trainer'] | false | electra-base-discriminator-finetuned-wnli This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6893 - Accuracy: 0.5634 | e1efb1b0f7dda8e776ffce39dd2e8811 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 0.6893 | 0.5634 | | No log | 2.0 | 80 | 0.7042 | 0.4225 | | No log | 3.0 | 120 | 0.7008 | 0.... | 30c3972644870eb4ad8a48325bafc00c |
mit | ['roberta-base', 'roberta-base-epoch_37'] | false | RoBERTa, Intermediate Checkpoint - Epoch 37 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 3e5c49076009e6a1a8baa83cbf1a2ba1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 5.6871 | 2178e67bf90faf1bdd03183541f6be7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 5.8660 | | No log | 2.0 | 2 | 5.7464 | | No log | 3.0 | 3 | 5.6871 | | a190184166536813f7acef409b43eb77 |
mit | [] | false | slm on Stable Diffusion This is the `<c-w388>` 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... | 375e4bdb84b2cba1d5437ef3e64685c7 |
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: 0.2140 - Accuracy: 0.926 - F1: 0.9258 | db87980a9c95e069f284a1a8fa259bc3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8453 | 1.0 | 250 | 0.3075 | 0.9115 | 0.9083 | | 0.2467 | 2.0 | 500 | 0.2140 | 0.926 | 0.9258 | | 2b5668dffbaa617baadc7cbe47c538fc |
apache-2.0 | ['generated_from_trainer'] | false | mrpc_bert-base-uncased_81 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6390 - Accuracy: 0.8088 - F1: 0.8717 - Combined Score: 0.8403 | ce88e5e574ddaa3c2bfbfeb28c86680d |
mit | ['generated_from_trainer'] | false | distracted_clarke This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tome... | cec9478611922d229744df08de4f32ed |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 5b2b094fc6dc70d19a749028f34b4571 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the dashdash concept trained by greasebig. This is a Stable Diffusion model fine-tuned on the dashdash concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of dashdash toy** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation pa... | c04844ea4b47d6b43121ce34b37e1af4 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `toy` images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. | 11167afb37a664852d9f894289dfa091 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-finetuned-squad_adversarial This model is a fine-tuned version of [stevemobs/distilbert-base-uncased-finetuned-squad](https://huggingface.co/stevemobs/distilbert-base-uncased-finetuned-squad) on the adversarial_qa dataset. It achieves the following results on the evaluation set... | 99178615058e176dbc9cb562343e3cac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6352 | 1.0 | 1896 | 2.2623 | | 2.1121 | 2.0 | 3792 | 2.2465 | | 1.7932 | 3.0 | 5688 | 2.3121 | | 8a46b1334cc7817d92e936cb933226e1 |
mit | ['exbert'] | false | PubMedBERT (abstracts + full text) Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pret... | 58286e8deeca09a487c89a865e9bd32e |
mit | ['exbert'] | false | Citation If you find PubMedBERT useful in your research, please cite the following paper: ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model P... | f01a0f33c35254f8193cc6d27680738f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aniAI Dreambooth model trained by guiza 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... | 8453858bcd62ffc4f3a7b62301188874 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.1458 | e87d16dd28a15869312f1f0d90a52c2a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2997 | 1.0 | 2767 | 1.1918 | | 1.0491 | 2.0 | 5534 | 1.1328 | | 0.8768 | 3.0 | 8301 | 1.1458 | | dfb2125bba1a4caa05106b9dfb83b006 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2189 - Accuracy: 0.923 - F1: 0.9230 | 6d00e7950f568757e592e636d9efc28d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8077 | 1.0 | 251 | 0.3160 | 0.9065 | 0.9051 | | 0.2462 | 2.0 | 502 | 0.2189 | 0.923 | 0.9230 | | eade159d7e94be203ddc073ba74fbeb2 |
unknown | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | A reupload of Systemy model finetuned with Cutesexyrobutts' arts Source: gofile(.)io/d/D1L69E Image examples: https://imgur.com/VPNUae8 Prompt and settings examples: https://huggingface.co/etherealxx/systemy-csrmodel-cutesexyrobutts/blob/main/Prompt%20and%20settings%20example.PNG Dreambooth settings used: ``` expo... | 29539c9b95355fc50f9145c42a12ba37 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | article2KW_test2.0.1c_lowercase_barthez-orangesum-title_finetuned_for_mlm_aaaaaaaaaaaaa This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0458 | 5c131b500bde536c8d92abccdf712a4f |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.488 | 1.0 | 685 | 0.0568 | | 0.0613 | 2.0 | 1370 | 0.0500 | | 0.0539 | 3.0 | 2055 | 0.0470 | | 0.0505 | 4.0 | 2740 | 0.0458 ... | 6aa350933792e86fad17a36791a1c787 |
mit | [] | false | UZUMAKI on Stable Diffusion This is the `<NARUTO>` 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 ... | 105d91548317d3d6ea197387312e7571 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/xlm-roberta-base-mrpc](https://huggingface.co/Intel/xlm-roberta-base-mrpc). | 67a9f879a89e3d885cb94437b882b9f7 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/xlm-roberta-base-mrpc-int8-dynamic') ``` | 48f2c9781657462d5f4eb50ae750c959 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Arabic (ar) 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](http... | e0eea6d5b20b2887843a50ad64fc07c8 |
mit | ['nlp', 'math learning', 'education'] | false | Math-RoBerta for NLP tasks in math learning environments This model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). MathRoBERTa has 24 layers, and 355 million parameters and its publis... | 19f4fce71f85365736c9bf4ff5b4827a |
mit | ['nlp', 'math learning', 'education'] | false | Here is how to use it with texts in HuggingFace ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('uf-aice-lab/math-roberta') model = RobertaModel.from_pretrained('uf-aice-lab/math-roberta') text = "Replace me by any text you'd like." encoded_input = tokeniz... | 276776d2a5dbf015abb46c501f6023a6 |
apache-2.0 | ['generated_from_keras_callback'] | false | FelipeAD/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: 4.0682 - Validation Loss: 3.3902 - Epoch: 7 | ace1cc0261652959248ffa961d848a7c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.0232 | 4.5431 | 0 | | 6.0233 | 3.9118 | 1 | | 5.2216 | 3.6621 | 2 | | 4.7560 | 3.5532 | 3 | | 4.4685 | 3.4825 | 4 | | 4.2748 |... | 9ec3a552a25cecc92322abd10d402737 |
apache-2.0 | ['translation'] | false | opus-mt-mfe-es * source languages: mfe * target languages: es * OPUS readme: [mfe-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mfe-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 822980abdd6c15f9d1104c3a260f9396 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ky', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-kyrgyz This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KY dataset. It achieves the following results on the evaluation set: - Loss: 0.5817 - Wer: 0.4096 | 87a21f0c12635de5823f7734ffea01fc |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ky', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.5412 | 18.69 | 2000 | 0.6161 | 0.5747 | | 1.311 | 37.38 | 4000 | 0.5707 | 0.5070 | | 1.1367 | 56.07 | 6000 | 0.5372 | 0.466... | 275856c17fd4b5547510729acdd5a76b |
mit | ['generated_from_trainer'] | false | stbl_clinical_bert_ft_rs7 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0848 - F1: 0.9208 | ae4f59610e560fad570b8044c3a6c978 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2755 | 1.0 | 101 | 0.0986 | 0.8484 | | 0.0655 | 2.0 | 202 | 0.0780 | 0.8873 | | 0.0299 | 3.0 | 303 | 0.0622 | 0.9047 | |... | d90d688d6d876a208f2da5c4328068fd |
mit | ['timelms', 'twitter'] | false | Twitter March 2020 (RoBERTa-base, 94M) This is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020. 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 Transformers interface. For an... | b620f12f5035e00f08f4c9c2baa79cc3 |
mit | ['timelms', 'twitter'] | false | Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-mar2020" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): token... | 6e69d22d277c938a78b16083e8abcc0c |
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-mar2020" tokenizer = AutoTokenizer.fro... | 2aafb371c8e648d7eb453a3d2f27ee71 |
mit | ['timelms', 'twitter'] | false | Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-mar2020" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) | 9b7e5afda7113d17d4e4c2d786c37f31 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper3_0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1844 - Train Accuracy: 0.0334 - Validation Loss: 0.5619 - Validation Accuracy: 0.0313 - Epoch: 19 | 52380a27e53b5f4f3d9f6bec89e953de |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0832 | 0.0116 | 4.4298 | 0.0124 | 0 | | 4.3130 | 0.0131 | 4.0733 | 0.0141 ... | 0c6eb4ed6396769066dfde72434386ac |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.1966 | eeae2c3edf9c5293ca7cc5c563c2f48c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2177 | 1.0 | 8853 | 1.1323 | | 0.8953 | 2.0 | 17706 | 1.1460 | | 0.7022 | 3.0 | 26559 | 1.1966 | | 269253ba46f97f7523f1f29fe720e858 |
mit | ['generated_from_trainer'] | false | poetry-gpt2-large-no_schiller_3 This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7301 | 38f1242939acc8bd18df9d93f56e2816 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 | b2a1670eec2f55bed258579219d370b6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.6925 | 1.0 | 20041 | 3.7494 | | 3.3496 | 2.0 | 40082 | 3.7301 | | b625bd5cbe9ae2e1e9723e9306381171 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_hubert_s456 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 40a4dbc56916488e0a871d6043b54287 |
mit | ['generated_from_trainer'] | false | hasoc19-xlm-roberta-base-sentiment-new This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3840 - Accuracy: 0.8726 - Precision: 0.8724 - Recall: 0.8726 - F1: 0.8725 | 9a404fb700516f615042e06264726d6f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4786 | 1.0 | 537 | 0.3999 | 0.8381 | 0.8391 | 0.8381 | 0.8363 | | 0.349 | 2.0 |... | d285b8761ee7381a74934a00c1100053 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/distilbert-base-uncased-padded_center_allsides_news_headlines 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: 1.8564 - Validation Loss: 1.7243 - Epoch: 8 ... | 0e6ef3554c30ec25eb1ca6a129a2cbc9 |
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... | c6a60ac3ccea3d62fdbaa3e84777117d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0100 | 1.8384 | 0 | | 1.9809 | 1.7614 | 1 | | 1.9691 | 1.8293 | 2 | | 1.9505 | 1.8739 | 3 | | 1.9408 | 1.8417 | 4 | | 1.9131 |... | ebd622b8597841fe9d6d6b0713a2a64f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Marathi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4618 - Wer: 41.6451 | 6ae067b1caaac4ceb9149c0cc5a0b760 |
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