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_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.76 | 1.0 | 2334 | 3.6658 | | 3.6325 | 2.0 | 4668 | 3.6454 | | 3.6068 | 3.0 | 7002 | 3.6428 | | 21bd3b49f835553c5c10d7df124495ad |
apache-2.0 | ['generated_from_keras_callback'] | false | avialfont/dummy-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: 5.6755 - Validation Loss: 3.8033 - Epoch: 2 | 10f1819619102e78bf59adf103129012 |
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': 5.6e-05, 'decay_steps': 3627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | 514d2f3b7a879dc87e1a23a1356f9b17 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2942 | 4.4915 | 0 | | 6.2878 | 3.9207 | 1 | | 5.6755 | 3.8033 | 2 | | a9396c97c77313035fffbf5662978db8 |
mit | ['generated_from_trainer'] | false | ClinicalTextV4 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5609 - Accuracy: 0.8658 - Precision: 0.8371 - Recall: 0.8939 - F1: 0.8646 | f8b26be91139e9584f3d65391bf8123d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4824 | 1.0 | 600 | 0.3630 | 0.8458 | 0.825 | 0.8609 | 0.8426 | | 0.3314 | 2.0 |... | 1e9690353947b47df0eb2dad35c98b32 |
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: 0.2301 - Accuracy: 0.9175 - F1: 0.9179 | ea516c063d1e8daa8bf2fdb5e73ae8d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8386 | 1.0 | 250 | 0.3275 | 0.904 | 0.9011 | | 0.2572 | 2.0 | 500 | 0.2301 | 0.9175 | 0.9179 | | 7d1ab44b936c80767c4d34e48c230f2a |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-fr_s22 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that y... | bac452b81fec7ccdab46a172420f0cc9 |
creativeml-openrail-m | ['Grapefruit', 'stable diffusion', 'stable diffusion diffusers', 'diffusers'] | false | **[Grapefruit](https://civitai.com/models/2583) by [ikena](https://civitai.com/user/ikena) (owner)** **grapefruit** (general hentai model) **lemon** (minimal weaker on nsfw, looks on some parts better then grapefruit) Grapefruit aims to be a hentai model with a bright and more „*softer*“ artstyle. Use a vae with ... | b03b17017b426321e9f714d82fa54371 |
creativeml-openrail-m | ['Grapefruit', 'stable diffusion', 'stable diffusion diffusers', 'diffusers'] | false | 1  ``` masterpiece, best quality, detailed, 1girl, brown hair, looking at viewer, long hair, city, (people), arms behind back, smile, sunny sky, house, street, cafe, Negative prompt: (worst quality, low qual... | a06ebe049f6bf8119245d618879d8378 |
creativeml-openrail-m | ['Grapefruit', 'stable diffusion', 'stable diffusion diffusers', 'diffusers'] | false | 2  ``` masterpiece, best quality, 1girl, black witch hat, grin, red eyes, black hair, medium hair, witch, (house on fire), looking at viewer, upper body, detailed, cleavage, face focus, sparkling eyes, Negati... | b16d6950623899dd9fc21037e4ab2885 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-10_female-0_s204 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | 9f4327289bcc48f9b77aac1a22c05c8b |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s396 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When usi... | 599f840d2958b42cc17c0ed358559b6d |
other | ['text-generation', 'opt'] | false | transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-iml-max-30b", torch_dtype=torch.float16).cuda() >>> | a656ee6b27b23838740b1efe5acab7f4 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-iml-max-30b", use_fast=False) >>> prompt = "What is the color of a carrot?\nA:" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> ... | 83ec7e0955df3387fd060de5b2cbc679 |
apache-2.0 | ['translation'] | false | opus-mt-mr-en * source languages: mr * target languages: en * OPUS readme: [mr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mr-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 21f089be0ac470536b027449868c72e1 |
mit | [] | false | Model desciption This is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/SpaceTransformers/CR/CR_ECSS_... | e7258ab268e34c70449308ca22df185b |
mit | [] | false | BibTeX entry and citation info ``` @ARTICLE{ 9548078, author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa}, journal={IEEE Access}, title={SpaceTransformers: Language Modeling for Space Systems}, year={2021}, volume={9}, number={}, pages={133111-133122}, doi={10.1109/ACCESS.2021.3115659} } ``` | 3f78dc9e672382ba2ad05e357aa38741 |
creativeml-openrail-m | ['text-to-image'] | false | cy02081 Dreambooth model trained by aichina with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks... | c23bbefe633372ec20c121f5726c854b |
agpl-3.0 | [] | false | word2vec model trained on Icelandic This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5) This model can not be loaded directly since it uses gensim, clone the repo... | b523013b9a1f372b910aba64e9e07284 |
agpl-3.0 | [] | false | Example output ```bash In [6]: model.wv.most_similar("england") Out[6]: [('wales', 0.8113704323768616), ('skotland', 0.7611601948738098), ('bretlandseyjar', 0.7280426621437073), ('gateshead', 0.6975484490394592), ('ástralía', 0.6963852047920227), ('eastbourne', 0.6939234137535095), ('englandi', 0.6908402442932... | c501abb167d7a588d0d5483e606b85ba |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Poison Model Welcome to poison model. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Unlike other anime style models, it has a little realistic style(but not too much), especially in the character painting. It's finetuned from [anything model](https://huggingface... | b70a959d5a47cc7d7b9fa4126da58ea9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you ... | f1dd53dff0cdb8b18e379ceeed0937af |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Lumine_DB Dreambooth model trained by Falon 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-diffu... | d3289dda146c24bdf490e4d260180460 |
apache-2.0 | [] | false | LongT5 (local attention, large-sized model) LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-re... | 2dfc8f0609d84a2326fe761603592017 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, LongT5Model tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-large") model = LongT5Model.from_pretrained("google/long-t5-local-large") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_s... | bef9cff2480ea54fb5c2e9392bb98f5d |
apache-2.0 | ['generated_from_trainer'] | false | medium-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/medium-mlm-imdb](https://huggingface.co/muhtasham/medium-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3883 - Accuracy: 0.9064 - F1: 0.9509 | aa5d81d2c024df3af79294af1434dbfd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2923 | 0.64 | 500 | 0.1860 | 0.9310 | 0.9642 | | 0.2049 | 1.28 | 1000 | 0.0830 | 0.9708 | 0.9852 | | 0.1569 |... | 0adc3b42250cac887b4b7e726cc80cfe |
apache-2.0 | ['translation'] | false | opus-mt-sv-uk * source languages: sv * target languages: uk * OPUS readme: [sv-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-uk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | af3b28d11783462916ec2b147fb8a211 |
apache-2.0 | ['generated_from_trainer'] | false | binary-classification 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: 0.3009 - Accuracy: 0.8968 | bcae2b1f8d751cf6e2444f0ce2339b1a |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-cola This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3381 - Matthews Correlation: 0.3994 | c7e9f134f78e21b0dd27b8a0f788debc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5491 | 1.87 | 500 | 0.6232 | 0.2182 | | 0.3596 | 3.73 | 1000 | 0.7203 | 0.3078 | | 0.2... | ffb13a486255aac2e6ff7f6d12792935 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-small-Urdu This model is a fine-tuned version of [Zaid/whisper-small-commonvoice](https://huggingface.co/Zaid/whisper-small-commonvoice) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5881 - Wer: 44.2387 | 0517000370da0e383c856c8a3e7795e6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5993 | 0.5 | 100 | 0.6257 | 37.9294 | | 0.352 | 1.35 | 200 | 0.5881 | 44.2387 | | c97c993a3da27e863c0aeed531ac3cac |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 500k (uncased) Seed 2 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 9130ea3140f9f84cea515d3beab7149e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-500k') model = BertModel.from_pretrained("multiberts-seed-2-500k") text = "Replace me by any text you'd like.... | e3a887e9d7ba76803266b934e4f07590 |
mit | [] | false | Junji Ito ArtStyle on Stable Diffusion This is the `<junji-ito-style>` 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.... | a4c438e50c5d9c3beb08256d4e25a079 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7737 - Accuracy: 0.9187 | 59169b694debd45b54c691faabe182cf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2909 | 0.7439 | | 3.7915 | 2.0 | 636 | 1.8815 | 0.83 | | 3.7915 | 3.0 | 954 | 1.1550 | 0.... | 050125e152624c4f378d914c9e1770b7 |
apache-2.0 | ['generated_from_trainer'] | false | AgitationTextV1 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: 0.5634 - Accuracy: 0.84 - Precision: 0.9054 - Recall: 0.8816 - F1: 0.8933 | 5e98504302823f75d9f02e4e84db1006 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.558 | 1.0 | 50 | 0.5387 | 0.75 | 0.9048 | 0.75 | 0.8201 | | 0.4431 | 2.0 |... | d9051834482a7033fef7d478f1835f2b |
apache-2.0 | ['generated_from_trainer'] | false | hasoc19-bert-base-multilingual-cased-profane-new This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5065 - Accuracy: 0.9030 - Precision: 0.8465 - Recall: 0.833... | 1b1343f7e9f26ec2ab22737ee4a5eafb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.2873 | 0.8954 | 0.8513 | 0.7881 | 0.8140 | | 0.2999 | 2.0 |... | 1a8ec708e27a045325af6cee3ace5523 |
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 imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3095 - Accuracy: 0.88 - F1: 0.8816 | 4851d82ba518135d2ce54e5c6c7e18cc |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-common_voice-tr-demo-dist This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - Wer: 0.3581 - Cer: 0.0805 | 271f94e548bf6cd59576a81a920e3eee |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - num_gpus: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 1 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: li... | 067f6ca0117125645b754e971da172f1 |
apache-2.0 | ['speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7391 | 0.92 | 100 | 3.5760 | 1.0 | | 2.927 | 1.83 | 200 | 3.0796 | 0.9999 | | 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 | |... | 97bfc81cb49c2fbb525bb04d6f21d141 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | `wav2vec2-large-xlsr-53-th` Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets) [Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd) We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53... | 9ddfbb57505796427d0b160e354d638a |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | `robust-speech-event` Add `syllable_tokenize`, `word_tokenize` ([PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)) and [deepcut](https://github.com/rkcosmos/deepcut) tokenizers to `eval.py` from [robust-speech-event](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-ev... | 1382b45e8c7eed39b7c3d07516be3e34 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | Eval results on Common Voice 7 "test": | | WER PyThaiNLP 2.3.1 | WER deepcut | SER | CER | |---------------------------------|---------------------|-------------|---------|---------| | Only Tokenization | 0.9524% | 2.5316% | 1.2346% | 0.1623% | | C... | 808bcc7313d6ed6857184e715dafafea |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | load pretrained processor and model processor = Wav2Vec2Processor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") model = Wav2Vec2ForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") | 616c1e0d8cddc6736188ae9d6c256162 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | function to resample to 16_000 def speech_file_to_array_fn(batch, text_col="sentence", fname_col="path", resampling_to=16000): speech_array, sampling_rate = torchaudio.load(batch[fname_col]) resampler=torchaudio.transforms.Res... | 3a6ccac3a9763ae4ec653d0fea031c96 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | infer with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) >> Prediction: ['และ เขา ก็ สัมผัส ดีบุก', 'คุณ สามารถ รับทราบ เมื่อ ข้อความ นี้ ถูก อ... | b68a8a9613b8508c7ee92b74ac835b65 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | Datasets Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with `pythainlp.tokenize.word_tokenize`. We preprocess the dataset using cleaning rules described in `notebooks/cv-preprocess.ipynb` by [@tann9949](https://gith... | d8b13cb5b5828d113aad2a9081b973c1 |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | create model model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-large-xlsr-53", attention_dropout=0.1, hidden_dropout=0.1, feat_proj_dropout=0.0, mask_time_prob=0.05, layerdrop=0.1, gradient_checkpointing=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_... | 54bc09c21c995a3d996488f84ee0ea9d |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | Evaluation We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evalu... | 17389487a50fbaf2d87cd9e20e87adea |
cc-by-sa-4.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'speech', 'xlsr-fine-tuning'] | false | Ackowledgements * model training and validation notebooks/scripts [@cstorm125](https://github.com/cstorm125/) * dataset cleaning scripts [@tann9949](https://github.com/tann9949) * dataset splits [@ekapolc](https://github.com/ekapolc/) and [@14mss](https://github.com/14mss) * running the training [@mrpeerat](https://gi... | 5046bde63869c5df4e041278f4bc5036 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-en-to-ro This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9912 | 92360919dc2d7f49bc0c4315d5af1a22 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 128 - total_train_batch_size: 512 - total_eval_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) a... | 6472e2001708f324d196eff602d41f8b |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 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: 0.5923 - Precision: 0.0039 - Recall: 0.0212 - F1: 0.0066 - Accuracy: 0... | 588b6c447c964bc50fcb59cfd35d4472 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6673 | 0.0476 | 0.0128 | 0.0202 | 0.6652 | | No log | 2.0 |... | fdf364b2fe013d7554774fd954f362a0 |
apache-2.0 | ['automatic-speech-recognition', 'cy', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-welsh 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 - CY dataset. It achieves the following results on the evaluation set: - Loss: 0.2650 - Wer: 0.2702 | 56a649318c6d3edbb87518d875eab54b |
apache-2.0 | ['automatic-speech-recognition', 'cy', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3000 - num_epochs: 50.0 - mixed_precision... | 005aa4fc3db1f73e6401e560a65f8206 |
apache-2.0 | ['automatic-speech-recognition', 'cy', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.3454 | 8.2 | 3000 | 0.4926 | 0.5703 | | 1.1202 | 16.39 | 6000 | 0.3529 | 0.3944 | | 1.0058 | 24.59 | 9000 | 0.3143 | 0.334... | 8e28313df1fb5c5c655b365f4f26c7ce |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_base_tf_384.in21k_ft_in1k An official MaxViT image classification model. Pretrained in tensorflow on ImageNet-21k (21843 Google specific instance of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/m... | 7bfb69c01205d1d8f313418c79ba5122 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 119.7 - GMACs: 73.8 - Activations (M): 332.9 - Image size: 384 x 384 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k - **Pretrain Dataset... | 049220b5e94a15e237346cc065322b09 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_base_tf_384.in21k_ft_in1k', pretrained=True) mo... | 0b917700a899c6c73557c3620a092503 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_base_tf_384.in21k_ft_in1k', pretrain... | ad48f60ca9e42c133823d45e87ab55a1 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_base_tf_384.in21k_ft_in1k', pretrained=Tru... | 222061e4abaccc6b8982521a0cd17e4f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-xas2-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. | cfedbc04db6a5c41023a7d4152e4bcce |
apache-2.0 | ['image-classification', 'vision'] | false | PoolFormer (M36 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-... | 6ba8db358961fe1d4cdda592eb33cbb2 |
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 PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/00000003976... | fa7ffbc0488e95655edd5d03203b6956 |
apache-2.0 | ['image-classification', 'vision'] | false | params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://hugg... | f06107d963c9cdc82189523a00e20f0d |
cc-by-4.0 | ['generated_from_trainer'] | false | deepset-bert-uncased-finetune This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on an unknown dataset. | 5d61874aaf4d201ceb419fd9a6ada398 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2-target-glue-qnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3630 - Accuracy: 0.8495 | ba668cdca2cdfec1878616aa6f691d33 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4855 | 0.15 | 500 | 0.3953 | 0.8254 | | 0.4445 | 0.31 | 1000 | 0.3830 | 0.8325 | | 0.4215 | 0.46 | 1500 | 0.3571 | 0.... | f637e7b5f297aa62cbf3f85c5d1718ab |
apache-2.0 | ['translation'] | false | opus-mt-eo-es * source languages: eo * target languages: es * OPUS readme: [eo-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/eo-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 2016266a2f91a3dc4dbbc45a054f9b5c |
apache-2.0 | [] | false | Dataset Description The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. ** Please no... | bb7e26e393815666be92f4997bae199c |
apache-2.0 | [] | false | Using the Model in the Transformer Library ``` from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("therealcyberlord/stanford-car-vit-patch16") model = AutoModelForImageClassification.from_pretrained("therealcyberlord/stanford-car-vit-patch1... | 226e09ab27c192afa31ea915e1247e89 |
apache-2.0 | [] | false | Citations 3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013. | 3d8a52846f53d6a24cdcd86959d102e2 |
mit | [] | false | smw map on Stable Diffusion This is the `<smw-map>` 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... | 90a230d10f1eb8ef99bc3753b8167f15 |
apache-2.0 | ['fill-mask'] | false | PaddlePaddle/mengzi-bert-base-fin PaddlePaddle version of [Langboat/mengzi-bert-base-fin](https://huggingface.co/Langboat/mengzi-bert-base-fin), please refer to the original model for more information | c3829b740a2b50fe1287116ff1bb9f85 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4218 | 3db5e10c8d39d0c7f08ce6bea31ad7fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2023 | 1.0 | 8157 | 1.2166 | | 0.9409 | 2.0 | 16314 | 1.3350 | | 0.7472 | 3.0 | 24471 | 1.4218 | | d91f2eb1001357ee48fa945a1a158085 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. | bb32818914421034e79a5c68457a4207 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Whisper Medium Bulgarian This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3423 - eval_wer: 18.7606 - eval_runtime: 1297.1096 - eval_samples_per_second: 1... | e78ca7fcc3191d1acc86354b1782d3e2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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... | 9ac6d9f211d21f3e5035fa6074b1906f |
apache-2.0 | [] | false | BART (large-sized model) BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://git... | caace5503e4923384d73b1fa32211ca1 |
apache-2.0 | [] | false | How to use Here is how to use this model in tf_transformers: ```python from tf_transformers.models import BartModel from transformers import BartTokenizer tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') model = BartModel.from_pretrained('facebook/bart-large') inputs_tf = {} inputs = tokenizer("Hel... | 07b081920fdeb778ee06c79db4337a12 |
apache-2.0 | ['generated_from_trainer'] | false | bert_emo_classifier This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3768 | 108eef45b219b8a3bb5927c91bd71409 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1497 | 0.25 | 500 | 0.2911 | | 0.1221 | 0.5 | 1000 | 0.3190 | | 0.108 | 0.75 | 1500 | 0.3343 | | 0.1296 | 1.0 | 2000 | 0.2803 ... | 9b07428d2c0cc0ecab1cb01afc0cf267 |
mit | ['generated_from_trainer'] | false | roberta-base-offensive-lm-tapt-finetuned This model is a fine-tuned version of [k4black/roberta-base-offensive-lm-tapt](https://huggingface.co/k4black/roberta-base-offensive-lm-tapt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4931 - F1: 0.7689 | e2043fe28aec51fe001625d61087eaeb |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 10 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 5 - mixed_precision_train... | 6694da9a30de48bd5a321d13fed3b27d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5239 | 0.08 | 100 | 0.4834 | 0.7377 | | 0.5121 | 0.16 | 200 | 0.4681 | 0.7404 | | 0.4579 | 0.25 | 300 | 0.4864 | 0.7632 | |... | 24b9cf31208c35bb2a3e75a18276e0c7 |
apache-2.0 | ['tapas'] | false | TAPAS large model fine-tuned on WikiSQL (in a supervised fashion) his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM ... | c7616c46a69dc4448943ad2f6ca450ec |
apache-2.0 | ['tapas'] | false | Model description TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available da... | 08c3e0d002eafaf98335ded7a72759e6 |
apache-2.0 | ['tapas'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Question [SEP] Flattened table [SEP] ``` The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts. | 9854256efb4c9fc704073a9326947792 |
apache-2.0 | ['tapas'] | false | Fine-tuning The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512. In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup ratio of 0.1424. See the [paper](https://arxiv.org/abs/2... | 9b3a60937e507f65eb21578e3de5f954 |
apache-2.0 | ['tapas'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, a... | 1a4412b6fed1e67b232f51b5a57c291d |
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