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 | 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... | b9286d678f80ac1d994d2546bd697a4a |
mit | ['generated_from_keras_callback'] | false | nouman10/robertabase-claims-2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5836 - Validation Loss: 0.3727 - Epoch: 1 | e4f31e291d8efb35cb0c7aa7b9d371f5 |
mit | ['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... | 7d9a5d712cf913d9602984e3c15d5854 |
apache-2.0 | ['masked-lm'] | false | HR-RoBERTa base model Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје. | 851e468cec8a59f4878d590444b6c59e |
apache-2.0 | ['masked-lm'] | false | Model description RoBERTa is a transformers model pre-trained on a large corpus of мацед data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs... | 74c70fa4e87bda67b8c773b0c1df565e |
apache-2.0 | ['masked-lm'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sen... | 0d7fe7f52da8d59f4c398c4886af7a58 |
apache-2.0 | ['masked-lm'] | false | How to use You can use this model directly with a pipeline for masked language modeling: \ from transformers import pipeline \ unmasker = pipeline('fill-mask', model='macedonizer/hr-roberta-base') \ unmasker("Zagrab je \\<mask\\> glavni grad Hrvatske.") \ [ {'sequence': 'Zagreb je glavni grad Hrvatske.', 'score'... | 2ad74742dfd5d3340b8cc16761ff8ca5 |
cc-by-sa-4.0 | ['translation'] | false | TakoMT This is a translation model using Marian-NMT. For more details, please see [my repository](https://github.com/s-taka/fugumt). In addition to the data listed in the repository I also used [ParaCrawl](https://paracrawl.eu/). * source languages: de, en, es, fr, it, ru, uk * target language: ja | 097cb1dd58d7c1ae856c7f6b70f92166 |
cc-by-sa-4.0 | ['translation'] | false | How to use This model uses transformers and sentencepiece. ```python !pip install transformers sentencepiece ``` You can use this model directly with a pipeline: ```python from transformers import pipeline tako_translator = pipeline('translation', model='staka/takomt') tako_translator('This is a cat.') ``` | 57daf23667f9beb6253b2fda25ccdf7d |
cc-by-sa-4.0 | ['translation'] | false | Eval results The results of the evaluation using [tatoeba](https://tatoeba.org/ja)(randomly selected 500 sentences) are as follows: |source |target |BLEU(*1)| |-------|-------|--------| |de |ja |27.8 | |en |ja |28.4 | |es |ja |32.0 | |fr |ja |27.9 | |it |ja |24.3 ... | ca207c7189693fb49c73a18fc5e25ce4 |
cc-by-sa-4.0 | ['audio', 'ConvTasNet', 'audio-to-audio'] | false | Clone from Asteroid model `JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: n_src... | f1eef747a7cc1df231c81b5d11d9f3cc |
mit | ['generated_from_trainer'] | false | rtm_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - Accuracy: 0.8667 | 419be245c28581731c9235dc15a22e46 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6955 | 0.09 | 50 | 0.6752 | 0.7867 | | 0.5362 | 0.19 | 100 | 0.4314 | 0.8333 | | 0.4065 | 0.28 | 150 | 0.4476 | 0.... | cc5fc0eb502e32df8f5b8b960353a67a |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-xls-r-common_voice-tr-ft-500sh This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.5794 - Wer: 0.4009 - Cer: 0.1032 | 6d4002af21c0920e89c94b2b68216fb2 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam wit... | 62d5c2308647616809ca63377cb65eca |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 0.5288 | 17.0 | 500 | 0.5099 | 0.5426 | 0.1432 | | 0.2967 | 34.0 | 1000 | 0.5421 | 0.4746 | 0.1256 | | 0.2447 | 51.... | 39c6b04534cc4c6028815e29991956c7 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_accent_surpeninsular-10_nortepeninsular-0_s222 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice... | 659026e6604d102818b364e4849ea333 |
apache-2.0 | ['translation'] | false | opus-mt-sq-en * source languages: sq * target languages: en * OPUS readme: [sq-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 29d7417869492d8c98642e272cac4151 |
openrail | [] | false | This is Remix an embedding for SD V2 768 Disclaimer: Remix is very experimental, it's possible to don't obtain good results, maybe changing the order of the elements that you want to mix or giving them more weight can help. Its use will be under your own risk. Remix can be used in 2 ways. As an artist using (prompt... | c67a625247878c4497ba28d567a2136f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | 80c9792299a6a6dc8a0c37f08224396e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-expense-ner This model is a fine-tuned version of [renjithks/distilbert-cord-ner](https://huggingface.co/renjithks/distilbert-cord-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2930 - Precision: 0.5096 - Recall: 0.4852 - F1: 0.4971 - Accuracy: 0.9275 | e640c147c472b4d9ee345b5748ebc61c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 22 | 0.3635 | 0.2888 | 0.0945 | 0.1424 | 0.8866 | | No log | 2.0 |... | 5a8dac48dc4450dd638d52aebcf8e647 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-sv_s1 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your ... | 451ddd6e930b6806e337b22826b5822b |
mit | [] | false | XGLM-1.7B XGLM-1.7B is a multilingual autoregressive language model (with 1.7 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Vic... | ca59284c0c288cd1dff50ff13a4a0225 |
mit | [] | false | tokens | ratio | ratio w/ lowRes upsampling | |:--------|:-----------------|:------------------------|-------------:|------------:|-------------:| | en | Indo-European | English | 803526736124 | 0.489906 | 0.3259 | | ru | Indo-European | Russian | 147... | 38101504f66bef5e026d306e49778cee |
mit | [] | false | Example (COPA) The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. ```python import torch import torch.nn.functional as F from transformers import XGLMTokenizer, XGLMForCausalLM tokenizer =... | 9e688aa562ec3886b8517f56d4b2e198 |
mit | [] | false | while 1 indicates that the second alternative is more plausible. def COPA_eval(prompt, alternative1, alternative2): lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() return 0 if lprob1 > lprob2 else 1 for lang in data_samples_long: for ... | 89e5e6b0d75b488982d649c6022f4480 |
apache-2.0 | ['summarization', 'generated_from_trainer', 'mt5'] | false | mt5_summarize_japanese (Japanese caption : 日本語の要約のモデル) This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) trained for Japanese summarization. This model is fine-tuned on BBC news articles ([XL-Sum Japanese dataset](https://huggingface.co/datasets/csebuetnlp/xlsum/viewe... | 3f33b13a02f3945be78897dc60a4eb3a |
apache-2.0 | ['summarization', 'generated_from_trainer', 'mt5'] | false | Intended uses ```python from transformers import pipeline seq2seq = pipeline("summarization", model="tsmatz/mt5-summarize-jp") sample_text = "サッカーのワールドカップカタール大会、世界ランキング24位でグループEに属する日本は、23日の1次リーグ初戦において、世界11位で過去4回の優勝を誇るドイツと対戦しました。試合は前半、ドイツの一方的なペースではじまりましたが、後半、日本の森保監督は攻撃的な選手を積極的に動員して流れを変えました。結局、日本は前半に1点を奪われましたが、途中出場の堂安... | 5bd7d2ac18c74ca0be71d7e383606106 |
apache-2.0 | ['summarization', 'generated_from_trainer', 'mt5'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 6cbc8ed39f21514e837901279631312e |
apache-2.0 | ['summarization', 'generated_from_trainer', 'mt5'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 4.2501 | 0.36 | 100 | 3.3685 | 0.3114 | 0.1654 | 0.2627 | 0.2694 | | 3.6436 | 0.72 | 200 ... | 8a9b36225f8ae620b0b3b3d4fd9429cf |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks 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: 0.6810 - Accuracy: 0.6471 | 1e1231f9b5afd260e4ee7677f39413f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 500 - nu... | 87e8bb4c6de59c54edcb654979442139 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 5 | 0.6810 | 0.6471 | | 0.6835 | 2.0 | 10 | 0.6785 | 0.6471 | | 0.6835 | 3.0 | 15 | 0.6748 | 0.... | 50e21cbfef7dbfdc27d552121f591a88 |
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: 0.2716 - Accuracy: 0.9385 - F1: 0.9382 | 842e7387a3e88db733eb55e30189c839 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.5485 | 1.0 | 16000 | 0.3088 | 0.933 | 0.9322 | | 0.2384 | 2.0 | 32000 | 0.2716 | 0.9385 | 0.9382 | | 23bf594c42552c7fedbe261399450f2c |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-scrambled-squad-15 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8722 | e5724960823f173838c140897d126aa9 |
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: 0.7755 - Accuracy: 0.9161 | bf10ff76a6cb3549c89d12ceadaf0da8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2893 | 1.0 | 318 | 3.2831 | 0.7403 | | 2.629 | 2.0 | 636 | 1.8731 | 0.8348 | | 1.5481 | 3.0 | 954 | 1.1581 | 0.... | baaa9cae88e6a6a1136b31ac3ddc94d5 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-en-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 2.7541 - Wer: 1.0 - Cer: 0.9877 | a319c19d7db8119cd299c0f98a0fcaef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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_sche... | 3a1e67808a1bda38037c7d5530c40dbb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:---:|:------:| | No log | 1.94 | 33 | 2.9905 | 1.0 | 1.0 | | No log | 3.88 | 66 | 2.9023 | 1.0 | 1.0 | | No log | 5.82 | 99 | 2.87... | c036aef25d6eb2f1d6be865b58baf322 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset. It achieves the following results on the evaluation set: - Loss: 0.1271 - Rouge2 Precision: 0.8165 - Rouge2 Recall: 0.7252 - Rouge2 Fmeasure: 0.761 | 772ed1d98237d7fc3f4d95f7917406d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.2002 | 1.0 | 4049 | 0.1609 | 0.7909 | 0.7013 | 0.73... | 157133a4223d325e4301e20a844672e2 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under [Model Access]( | 5a9390430fa5520d844f739989f8919c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion Version 1 For the first version 4 model checkpoints are released. *Higher* versions have been trained for longer and are thus usually better in terms of image generation quality then *lower* versions. More specifically: - **stable-diffusion-v1-1**: The checkpoint is randomly initialized and has bee... | b3d99adf8ee187bf2182c53b8673facf |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Model Access Each checkpoint can be used both with Hugging Face's [ 🧨 Diffusers library](https://github.com/huggingface/diffusers) or the original [Stable Diffusion GitHub repository](https://github.com/CompVis/stable-diffusion). Note that you have to *"click-request"* them on each respective model repository. | **... | 6b5f57955df8094f7f8fb79e1e6d187b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | License [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initia... | 27e97ebccd82550cae27def1eaaba617 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-0_sixties-10_s695 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | e4c0ebeed41a601760a301b29ee76a3a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_mnli This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4768 - Accuracy: 0.... | 0c719b7527b24ad98525714a6b660fcb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5762 | 1.0 | 1534 | 0.5291 | 0.5904 | | 0.5131 | 2.0 | 3068 | 0.4986 | 0.6470 | | 0.4806 | 3.0 | 4602 | 0.4832 ... | 8efedf723ff66018ecb9d5d5e4edee0e |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-es_s555 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 994b93eb3585ffd4ee21e1ea35ab2aa5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0595 - Precision: 0.9343 - Recall: 0.9504 - F1: 0.9423 - Accuracy: 0.9866 | e10ac9af95e4bceab5285ca32b500974 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0834 | 1.0 | 1756 | 0.0621 | 0.9148 | 0.9381 | 0.9263 | 0.9833 | | 0.0321 | 2.0 |... | cf07f7a4420ad72e2ed76bf6f3584948 |
apache-2.0 | ['seq2seq', 'lm-head'] | false | t5-base-dutch Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/) & [Dat Nguyen](https://www.linkedin.com/in/dat-nguyen-49a641138/) during the [Hugging Face community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [Hug... | 0d7e921cf3c1051429d67d7e7dc181bf |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Dataset This model was trained on the `full` configuration of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), which is the original mC4, except * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://git... | 1b3f00e325064367d5e49353fe51293e |
apache-2.0 | ['seq2seq', 'lm-head'] | false | Training The model was trained on the `full` mc4_nl_cleaned dataset configuration for 1 epoch, consisting of 34B tokens, for 528 482 steps with a batch size of 128 and took 57 hours. A triangle learning rate schedule was used, with peak learning rate 0.005. | 68128c28806e28285b56f4c0bfc12233 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-wnli-custom-tokenizer-expand-vocab 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: 0.1184 | 7ab53451a46757571a05b585596c3f11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3425 | 6.25 | 500 | 2.3392 | | 1.9269 | 12.5 | 1000 | 1.5744 | | 1.2226 | 18.75 | 1500 | 1.1470 | | 0.8299 | 25.0 | 2000 | 0.9460 ... | f1f3cad3a5f0053faeb4b57818c266a3 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-common-voice-50p-persian-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: 1.0939 - Wer: 0.6537 | a9e029e33f9bde086ec07cebe4cb83db |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 40 - mixed_precision_... | 1b194be0abf6222d005bcee454c55b4c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0437 | 2.52 | 600 | 3.0170 | 1.0 | | 2.3667 | 5.04 | 1200 | 2.1575 | 0.9988 | | 0.9565 | 7.56 | 1800 | 1.0801 | 0.8410 | |... | 3504c8f9d21e054bfef5f3ea401fb467 |
mit | ['generated_from_trainer'] | false | mis_515_xlnet This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5905 - Accuracy: 0.862 | 462555465d6ac2b4b3adb6bd509bfa0e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 746e532397d7d3f1dc1f761170c237dc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5316 | 1.0 | 1125 | 0.6284 | 0.813 | | 0.4047 | 2.0 | 2250 | 0.5905 | 0.862 | | 99728f4cc94bd847d2d12d8807dafac5 |
apache-2.0 | ['t5', 'pytorch', 'zh'] | false | T5 for Chinese Spelling Correction Model 中文拼写纠错模型 `shibing624/mengzi-t5-base-chinese-correction` evaluate SIGHAN2015 test data： - Sentence Level: precision:0.8321, recall:0.6390, f1:0.7229 训练使用的数据集为下方提供的“SIGHAN+Wang271K中文纠错数据集”，在SIGHAN2015的测试集上达到接近SOTA水平。 未改动模型结构，finetune中文纠错数据集，评估纠错效果很好，模型潜力巨大。 | 76108605391cac6eebea500ccadd8a01 |
apache-2.0 | ['t5', 'pytorch', 'zh'] | false | Usage 本项目开源在中文文本纠错项目：[pycorrector](https://github.com/shibing624/pycorrector)，可支持t5模型，通过如下命令调用： ``` pip install -U pycorrector ``` run: ```python from pycorrector.t5.t5_corrector import T5Corrector nlp = T5Corrector("shibing624/mengzi-t5-base-chinese-correction").batch_t5_correct i = "今天新情很好" print(i, ' => ', nlp([i... | 783b55ce6f3b063c37170ce55996569c |
apache-2.0 | ['t5', 'pytorch', 'zh'] | false | SIGHAN+Wang271K中文纠错数据集 | 数据集 | 语料 | 下载链接 | 压缩包大小 | | :------- | :--------- | :---------: | :---------: | | **`SIGHAN+Wang271K中文纠错数据集`** | SIGHAN+Wang271K(27万条) | [百度网盘（密码01b9）](https://pan.baidu.com/s/1BV5tr9eONZCI0wERFvr0gQ)| 106M | | **`原始SIGHAN数据集`** | SIGHAN13 14 15 | [官方csc.html](http://nlp.ee.ncu.edu.tw/resour... | 862ce0a878ce59dcf6f10a63cf433114 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-bert-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4148 - Accuracy: 0.8603 - F1: 0.9036 | 4b29a2bb141aaa7856b7eabc6887f70a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5315 | 1.0 | 230 | 0.3698 | 0.8382 | 0.8862 | | 0.3 | 2.0 | 460 | 0.3677 | 0.8431 | 0.8919 | | 0.1575 |... | 998513fa6ad7360dbb7965be230c1378 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-cola This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5412 - Matthews Correlation: 0.4140 | 7c1917a3fe990e2e6decb1a30be76c8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5329 | 1.87 | 500 | 0.6440 | 0.2518 | | 0.3419 | 3.73 | 1000 | 0.7266 | 0.3317 | | 0.2... | 5d60744725c2a4e808f756d04e831d43 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_wnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3434 - Accuracy: 0.5634 | 18b347105d16f66ebb3bcbcffe8b3abc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5945 | 1.0 | 3 | 0.3447 | 0.5634 | | 0.4026 | 2.0 | 6 | 0.3814 | 0.4366 | | 0.3533 | 3.0 | 9 | 0.3511 | 0.... | 6c9160eda3ff5e9caf8579b3accef3f8 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the Kiri concept trained by rithviknishad on the rithviknishad/kiri dataset. This is a Stable Diffusion model fine-tuned on the Kiri concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Kiri Avatar** This model was created as part of the DreamBooth Hackathon �... | e3015f32ffef2ff7c503404e7f5b4661 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings Wav2Vec2-Conformer with relative position embeddings, pretrained and **fine-tuned on 960 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Paper**: [fairseq S2T: Fast ... | a5204abc3b7cd89a2966ec9218bd46ea |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft") | a574446d185f51bf17232e91641f91b5 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-conformer-rel-pos-large-960h-ft** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ConformerForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_... | b74b0148d24fb31c0f5f7323f08f465a |
mit | [] | false | Geggin21 on Stable Diffusion This is the `<geggin>` 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... | a76421549055fa92922a3a85609f87c1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Nona-is-my-muse Dreambooth model trained by smitpatl 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-sta... | 89404d703eeaea09793143e0005e0977 |
mit | [] | false | PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization <a href="https://github.com/shunsukesaito/PIFu" target="_blank">https://github.com/shunsukesaito/PIFu</a> This a checkpoint from the original project here are some important <a href="https://github.com/shunsukesaito/PIFu | 7997fc5af6118328afcd8b375ab9eca5 |
mit | [] | false | demo" target="_blank">notes</a>: > Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data. ``` @InProceedings{saito2019pifu, author = {Saito, Shunsuke... | a4da48bf7b13be59d4a3ea751220c1bb |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mt5-small-dequad-qg-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.c... | d97b88ac2ecf341ad6b5711aea0fedbb |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** de - **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417... | 4f9b64e0776cc2234981284eabb6fb93 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-dequad-qg-ae... | 5ad3d79bc9c7583a7f196877a3e1c926 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | question generation question = pipe("extract answers: Sommerzeit <hl> Frühling <hl>: Umstellung von Normalzeit auf Sommerzeit – die Uhr wird um eine Stunde ''vor''gestellt. Herbst: Umstellung von Sommerzeit auf Normalzeit – die Uhr wird um eine Stunde ''zurück''gestellt. Als Sommerzeit wird die gegenüber der Zonenzeit... | b002b8887b0ba52784a95da4c318bc39 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | |:--... | 877aa5a38eef228bd80e8e41d808dd77 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 ... | 21a115387057f12795aebfeb06b23fba |
afl-3.0 | [] | false | What is it? PyCodeGPT is efficient and effective GPT-Neo-based model for python code generation task, which is similar to [OpenAI Codex](https://openai.com/blog/openai-codex/), [Github Copliot](https://copilot.github.com/), [CodeParrot](https://huggingface.co/blog/codeparrot), [AlphaCode](https://deepmind.com/blog/ar... | ef482f92a21e16ba43c7ffbffc3c3b32 |
afl-3.0 | [] | false | Training Data Due to the small size of public released dataset, we proposed to collect data from GitHub from scratch. We first crawled 1.2M python-related repositories hosted by GitHub. Then, we used these repository URLs to download all contents of each repository from GitHub. After that, we got 60M raw python files ... | 5925992dfef876d820346b45e3fe28c7 |
afl-3.0 | [] | false | Evaluation Results Here's our evaluation result on HumanEval dataset: Note: our model can have a comparable accuracy with Codex of similar model size. |Model|Pass@1|Pass@10|Pass@100| |:------:|:---:|:---:|:---:| |PyCodeGPT-110M |**8.32%** |**13.53%** |**18.3%** | ||||| |GPT-Neo 125M ... | adb37f1ac744c0ab5f44f6a66dffd42e |
afl-3.0 | [] | false | Reference If you want to use the models, you need to cite our following paper: ``` @inproceedings{CERT, title={{CERT}: Continual Pre-training on Sketches for Library-oriented Code Generation}, author={Zan, Daoguang and Chen, Bei and Yang, Dejian and Lin, Zeqi and Kim, Minsu and Guan, Bei and Wang, Yongji and Chen... | d6d01c52c866d4fe5c55ef5ec3d41533 |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer v2 (tiny-sized model) Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micros... | a95a4f10c1435f6009341d472fe130f2 |
apache-2.0 | ['vision', 'image-classification'] | 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 AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | da49b876fcf803e0044f77392b0f3018 |
apache-2.0 | ['translation'] | false | fr-he * source group: French * target group: Hebrew * OPUS readme: [fra-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-heb/README.md) * model: transformer * source language(s): fra * target language(s): heb * model: transformer * pre-processing: normalization + SentencePiece (spm32... | 880f38dfc46c15fa7184e015ccaff87a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fr-he - source_languages: fra - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'he'] - src_constituents: ('French', {'fra'}) -... | 97ad02b865bac25e3481bc4273407b56 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Indonesian This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.2409 - Wer: 18.2837 | ee357d6ca845a6d0886feb0b934b7e75 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.4103 | 0.66 | 1000 | 0.3802 | 27.0497 | | 0.2682 | 1.32 | 2000 | 0.3223 | 22.9365 | | 0.2381 | 1.99 | 3000 | 0.2884 | 2... | 7ee7eda2da85711581d8a0633972274d |
mit | ['generated_from_trainer'] | false | romantic_bose This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-25000... | cda2000b4ab48890d892133ffc4388d2 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | aa57dcf11085c3f296b35e7b14807a7b |
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