license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
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-small-subjqa-v... | 113e7e96e1d38a2de381a2e7cf99bc9c |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset ... | 3b79691284e239f7d2484fa660a66e4c |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 32... | 11b1ac4f8fef842de8e588b586ed678e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Bulgarian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 bg dataset. It achieves the following results on the evaluation set: - Loss: 0.3208 - Wer: 13.4040 | 0966bae1834f977fe3bd59e1b8269d19 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0023 | 7.04 | 1000 | 0.3208 | 13.4040 | | 89f86a217b26e1ef92eec6366a95cb78 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Model description This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. This model is finetuned using GPT2... | 30dfadd8c83eee537b8cea070aebe523 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Training data I used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. I used tweets from both ... | 8c59a70db6257315b1e382f78b15f8f0 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Bulgarian (bg) 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](h... | d19c4e7ab925a2bbe4b246fffd3205cd |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | wav2vec2-xlsr-300M-NPSC-OH This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1692 - Wer: 0.1663 | d4d871ab3002c803ade20ac44faa3a3c |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', '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: 16 - seed: 13 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 04926ae7a231a9cf69c1515c517e95b4 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.1638 | 0.66 | 500 | 3.0686 | 1.0 | | 2.9311 | 1.31 | 1000 | 2.9208 | 1.0 | | 2.4175 | 1.97 | 1500 | 1.5009 | 0.904... | ffdeed722137f74d2273e4b85b849757 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Vocoder with HiFIGAN trained on custom German dataset This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained on a generated German dataset using [mp3_to_training_data](https://github.com/padmalcom/mp3_to_training_data). The pre-trained model (8 epochs... | 418550b24c5eeb3baa94725e8aaa63c2 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | How to use Install speechbrain. ```bash pip install speechbrain ``` Use a TTS model (e.g. [tts-tacotron-german](https://huggingface.co/padmalcom/tts-tacotron2-german)), generate a spectrogram and convert it to audio. ```python import torchaudio from speechbrain.pretrained import Tacotron2 from speechbrain.pretrain... | 0f453675baee558e54e2c32be3955a83 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola 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: 0.7696 - Matthews Correlation: 0.5136 | ae6b3f2425813092a449570435d1d9ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5284 | 1.0 | 535 | 0.4948 | 0.4093 | | 0.3529 | 2.0 | 1070 | 0.5135 | 0.4942 | | 0.2... | 4a7e2a587a12fe569c13f10cfef480cd |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-5_sixties-5_s279 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 06185cb9fa2aa56040f1f7fba902e708 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.9327 - Mean Iou: 0.0763 - Mean Accuracy: 0.1260 - Overall Accuracy: 0.592... | 592e9cf7e5aad922d0ce56f7df918f4a |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - num_epochs: 0.05 | eeec123a79b5e7a41c8a0d10b2baad6b |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 9e996d74ab5c4d3e60f84afe473f466f |
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: 0.2173 - Accuracy: 0.925 - F1: 0.9252 | 0f4e208afe18f0b696eaa0c2c313defa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.825 | 1.0 | 250 | 0.2925 | 0.915 | 0.9134 | | 0.2444 | 2.0 | 500 | 0.2173 | 0.925 | 0.9252 | | f3676fc6498d3ceb8fb97a49e232164a |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-cnndm_fs0.1-c This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9852 - Recall: 34.0438 - Precision: 33.1906 - F1: 31.9429 - Gen Len: 18.9962 | 85978488a02534d6878529accc4f7376 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 86a9ecbcf46f1010476f343e36bb499a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-------:|:-------:| | 2.19 | 0.11 | 200 | 1.6689 | 33.0433 | 41.8436 | 35.485 | 18.9695 | | 1.74 | 0.23... | f087a0f6a3e5110b727904a9596c785f |
apache-2.0 | ['generated_from_trainer'] | false | bart-model2-1209 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Rouge1: 55.8035 - Rouge2: 46.8603 - Rougel: 54.6759 - ... | 779e86a9c68b8d4d492b19d4b3cd2174 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9329 | 1.0 | 650 | 0.1658 | 55.8035 | 46.8603 | 54.6759 | 55.2072 | 19... | c5d6c948d9aa3df75797a4914799c886 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | abrazaq Dreambooth model trained by raza2 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head o... | 5ef2ad332d1fc782dabccd41b9946f13 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-squad This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0014 | d420d8bf5663dbe6212f0d1f3b9b166f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0927 | 1.0 | 5536 | 1.0290 | | 0.87 | 2.0 | 11072 | 0.9683 | | 0.7335 | 3.0 | 16608 | 1.0014 | | 6d12c2443643595d93f4bdff962f1135 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 - Accuracy: 0.9748 | 7715edee87f18312627b1fd1c0f8e607 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.141 | 1.0 | 190 | 0.1496 | 0.9544 | | 0.0736 | 2.0 | 380 | 0.0958 | 0.9719 | | 0.0568 | 3.0 | 570 | 0.0727 | 0.... | 2cc97971d0138fd2ebccac70849ade5e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-2_england-8_s930 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | b61c1ff362aca58a02bf40794bdbd6dd |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Introduction mT5-base-en-msmarco-v1 is a mT5-based model finetuned on English MS MARCO passage dataset. Further information about the dataset or the translation method can be found on our paper [**mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](... | 2c3c6ef27b1522334ec0c7b7b7a80b0b |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Usage ```python from transformers import T5Tokenizer, MT5ForConditionalGeneration model_name = 'unicamp-dl/mt5-base-en-msmarco' tokenizer = T5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) ``` | 0416e7785b64d16067f5badabba4d1bd |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'en'] | false | Citation If you use mT5-base-en-msmarco, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo... | 61f6c333c3b7bea5db6875fbbf06ae16 |
apache-2.0 | ['generated_from_trainer'] | false | twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3190 - Precis... | 53175adbd1947d05d37679c638a838f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4963 | 0.0223 | 0.0562 | 0.0319 | 0.7461 | | No log | 2.0 |... | c8a6ae42dca172549a009aced9fdc87c |
apache-2.0 | ['generated_from_keras_callback'] | false | risethi/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: - Train Loss: 0.9709 - Validation Loss: 1.1167 - Epoch: 1 | 8b486f1e60a16eab94afc158b6dd7753 |
mit | ['generated_from_trainer'] | false | bert_base_tcm_0.9_10_epochs This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0190 - Criterio Julgamento Precision: 0.8170 - Criterio Julg... | 27ab0ac16eabe59ad50b4f1761ceea56 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 10.0 | 610d409b81958450716883cbb928bcc9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitac... | e9d2b3aab05a620ef03abe47bd724aff |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7272 - Matthews Correlation: 0.0899 | 689191520f363c56e389660b574ac3f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6097 | 1.87 | 500 | 0.6214 | 0.0 | | 0.601 | 3.73 | 1000 | 0.6166 | 0.0 | | 0.5... | 5cdd734b071cc094e7836450f220b9fe |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion Embedding by ConflictX For SD 2.0 trained on 768x768 images from midjourney. Install by downloading the step embedding you want, and put it in the \embeddings folder It is slightly overfit on 150 steps so some concepts/keywords will be harder to prompt for (use negatives or weight Kipaki down) bu... | 0413dadddbb7d7e0593d8108e4fa2f5a |
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_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3206 | 3a44e5b1a3f74181441d15cbc47809a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2156 | 1.0 | 8235 | 1.1791 | | 0.9413 | 2.0 | 16470 | 1.2182 | | 0.7514 | 3.0 | 24705 | 1.3206 | | a7917c407ca5e10a0f215619ff3b3d20 |
agpl-3.0 | ['generated_from_trainer'] | false | XLMR-ENIS-finetuned-stsb This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5232 - Pearson: 0.8915 - Spearmanr: 0.8888 | dceaef44d8eedef3d9140409e3480fc5 |
agpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6330 | 0.8562 | 0.8570 | | 1.2835 | 2.0 | 720 | 0.6368 | 0.8790 | 0.8781 | | 0.4518 ... | 0684143a457ac423321d2bb500d70c06 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-nyanja-test_v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.3734 - Cer: 0.0827 | 2ad001cb657a9db54dd4723f84f236bd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 542188dc9cc5004dd3eb71a9ec0a91fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.5816 | 0.62 | 400 | inf | 0.5702 | 0.1373 | | 0.6341 | 1.24 | 800 | inf | 0.4383 | 0.1022 | | 0.5103 | 1.86 |... | d3615367e52b6058cbf7111f8e22b21b |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-etc-nlp This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0039 - Accuracy: 0.9993 - F1: 0.9993 | c96f023b3eda0d99b3a12915110c1870 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 262 | 0.0025 | 0.9997 | 0.9997 | | No log | 2.0 | 524 | 0.0039 | 0.9993 | 0.9993 | | 6b8542b504211b5b5fbb4949aea6e08a |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-5_sixties-5_s408 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 using th... | abaf00c4b6d2d6bef5b2b76e4f9c34cd |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech-sat_s692 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) 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 spee... | 933883a31719f00b950e6c0675428f0d |
apache-2.0 | ['generated_from_keras_callback'] | false | kookoobear/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8602 - Validation Loss: 2.6150 - Epoch: 0 | 65dbf732f61e7ef4d9575345512ccefa |
mit | ['generated_from_trainer'] | false | run-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: - Loss: 2.1449 - Accuracy: 0.75 - Precision: 0.7115 - Recall: 0.7093 - F1: 0.7103 | 89bf4cdd108cd6b1a4cf08879f5001c3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9838 | 1.0 | 50 | 0.8621 | 0.645 | 0.6536 | 0.6130 | 0.6124 | | 0.7134 | 2.0 |... | f160a1ccc6ae96ea8a45ba15e89b46d4 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | luigisaetta/whisper-medium 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: - Loss: 0.1531 - Wer: 5.5543 | 68bdc98b928dc42c3e95319f51105d29 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | 56055e7ec8bc02753c5044f84dbdc1be |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2023 | 0.17 | 1000 | 0.1852 | 7.6354 | | 0.1215 | 0.33 | 2000 | 0.1577 | 6.4088 | | 0.0711 | 1.1 | 3000 | 0.1576 | 6.1324 | |... | 660df7c2af12a027ea626152a6ce3e7d |
apache-2.0 | [] | false | PaddlePaddle/uie-senta-micro Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personaliz... | 94d3200679d2a7c66db059fddbb6d914 |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_svae") ``` | 44d4aa822d75ac298fd877477e8fc2bc |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93.13 (0.01) | 93.16 (0.31) | [1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and J... | f8c2e940cb4dee8cc8d3227726ea4e94 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-emotion This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1342 - F1: 0.9365 | decf57e3a561872bb6465e25c890cd37 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7357 | 1.0 | 250 | 0.2318 | 0.9224 | | 0.1758 | 2.0 | 500 | 0.1679 | 0.9349 | | 0.1228 | 3.0 | 750 | 0.1385 | 0.9382 | |... | 5e28be623c085e126e31f1e04b476364 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Darija) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-darija", savedir="pretrained_models/asr-wav2vec2-dvoice-dar") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` | 531d5b7bacd0f9326a04824097c69162 |
mit | ['generated_from_trainer', 'deberta-v3'] | false | DeBERTa v3 (small) fine-tuned on SST2 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2134 - Accuracy: 0.9404 | 01ac19bc7a753b128f861fdaae5a732e |
mit | ['generated_from_trainer', 'deberta-v3'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.176 | 1.0 | 4210 | 0.2134 | 0.9404 | | 0.1254 | 2.0 | 8420 | 0.2362 | 0.9415 | | 0.0957 | 3.0 | 12630 | 0.3187 ... | 0fc1a35aba9681f84bc8f59a1ba64857 |
mit | [] | false | Ancient Greek BERT finetuned for tagging and parsing PROIEL (UD) This is a finetuned checkpoint of [Ancient Greek BERT](https://huggingface.co/pranaydeeps/Ancient-Greek-BERT) by Singh, Rutten and Lefever (2021), which has been trained on the [UD version of PROIEL](https://github.com/UniversalDependencies/UD_Ancient_G... | e49b940f6e4a0f29212b1803449af09a |
mit | [] | false | Performance This is the performance on the [test set of the UD version of PROIEL](https://github.com/UniversalDependencies/UD_Ancient_Greek-PROIEL/blob/master/grc_proiel-ud-test.conllu). | Metric | Accuracy | |:--|:--| | UPOS | 0.9814480997446298 | | XPOS | 0.9821991888237945 | | feats | 0.9254168544389365 | | all t... | c036267111e93af9c5bab16c92795db3 |
mit | [] | false | Bibliography - Singh, P., Rutten, G. Lefever, E. 2021. A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek. Proceedings of LaTeCH-CLfL 2021, pp. 128β137. [https://doi.org/10.18653/v1/2021.latechclfl-1.15](https://doi.org/10.18653/v1/2021.latechclfl-1.15). | 7243f681c74a4b11bcd588adac903f82 |
mit | ['generated_from_trainer'] | false | clinical-finetuned-data3 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: 0.5058 - Accuracy: 0.86 - Precision: 0.875 - Recall: 0.9265 - F1: 0.9 | 6cdd4c64352e30f7420ca3f93da5bd6d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | f9596d239b1e38c9f37fa873ce32a409 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech-ml_s784 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) 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... | 53bdda7f21af09831aa3202bd99f34ba |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Woolly Dreambooth model trained by LaCambre 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... | 671b4a9f25280c9925ad7dfc3f155de8 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-FF9000 (Deep-Narrow version) T5-Efficient-BASE-FF9000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoin... | fb44774e3c6435ad9c6fbec4ce6e435b |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-ff9000** - is of model type **Base** with the following variations: - **ff** is **9000** It has **449.42** million parameters and thus requires *ca.* **1797.7 MB** of memory in full precision (*fp32*) or **898.85 MB** of memory in half precisio... | cecf2e9361cf991511f5e4389a828439 |
apache-2.0 | ['generated_from_trainer'] | false | depression_suggestion This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3740 | a516b767a22819f313cc5189a4e5a85a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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: 70 | 39d9ed5b45dbf0e61ccf9ac589db03d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 60.7965 | | No log | 2.0 | 6 | 60.5778 | | No log | 3.0 | 9 | 60.1954 | | No log | 4.0 | 12 | 59.6487 ... | 07f177fe3cece1c4c67db341b3531e74 |
mit | ['translation'] | false | mBART 25 SentencePiece tokenizer This tokenizer is used for Mideind's mBART translation models. It is based on Facebooks mBART-25 SentencePiece model. A language token from the original model has been replaced with "is_IS". Usage example (for debugging): ```python import sys from transformers.models import mbart MO... | a225d1438bfee7cdaa9569186c3db6c0 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-squad-qg-no-paragraph` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-... | 033305e8c27086bad333c02151fba583 |
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-small-squad-qg... | 5799be9bce024b58c4e41c3709d826df |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 02442bc0d9250a57db9741dda7f52943 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 128 - max_length_output: 32 - epoch: 8 - batch: 64... | 3860406e127067a6fc372edab2537edc |
cc-by-4.0 | [] | false | FinEst BERT FinEst BERT is a trilingual model, using bert-base architecture, trained on Finnish, Estonian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledg... | 4aa12f02d3228f3203d0c73ebeb62a2d |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-squad2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9325 | f221a98704774d4ea505889864743eb0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.88 | 1.0 | 8160 | 0.8129 | | 0.6643 | 2.0 | 16320 | 0.8567 | | 0.5096 | 3.0 | 24480 | 0.9325 | | ec168572935034e0068c1ad01fbd359b |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-NL24 (Deep-Narrow version) T5-Efficient-SMALL-NL24 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | f50f4d7f789ac3caf53c3c1f424cb49a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-nl24** - is of model type **Small** with the following variations: - **nl** is **24** It has **192.73** million parameters and thus requires *ca.* **770.92 MB** of memory in full precision (*fp32*) or **385.46 MB** of memory in half precision ... | 295b5e3e4f6f85a36b86ceb1ca52a88a |
mit | ['T5', 'Seq2Seq', 'EconderDecoder', 'Spanish'] | false | Spanish T5 (small) trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus). This is a Spanish **T5** (small arch) trained from scratch on the [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus) aka BETO's corpus with [Flax](https... | 4b033f2c63be844d2a9d0bf42dd1edbf |
mit | ['T5', 'Seq2Seq', 'EconderDecoder', 'Spanish'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2021spanish-t5-small, title={Spanish T5 (small) by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/flax-community/spanish-t5-small}}, ye... | a41a4b6064939a4bd65b488ec04f8481 |
apache-2.0 | ['fill-mask', 'korean', 'lassl'] | false | How to use
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lassl/bert-ko-base")
tokenizer = AutoTokenizer.from_pretrained("lassl/bert-ko-base")
```
| 33b19209ed361436f1b8c069a8b6d627 |
apache-2.0 | ['fill-mask', 'korean', 'lassl'] | false | Corpora
This model was trained from 702,437 examples (whose have 3,596,465,664 tokens). 702,437 examples are extracted from below corpora. If you want to get information for training, you should see `config.json`.
```bash
corpora/
βββ [707M] kowiki_latest.txt
βββ [ 26M] modu_dialogue_v1.2.txt
βββ [1.3G] m... | a7a1403a6133f771aa55cf44b843377b |
apache-2.0 | ['generated_from_trainer'] | false | test-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1014 - Precision: 0.9609 - Recall: 0.9574 - F1: 0.9591 - Accuracy: 0.9732 | 707195b849270bf76fc32a2f22019731 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1848 | 0.9060 | 0.9184 | 0.9122 | 0.9490 | | No log | 2.0 |... | 3082f3aeeec5bb41e2040b9269f1dfe6 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-detests-02-11-2022 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8124 - F1: 0.6381 | 6d82e063df6eaf864615e560a8e1afbb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.379 | 0.64 | 25 | 0.4136 | 0.0 | | 0.315 | 1.28 | 50 | 0.3663 | 0.6343 | | 0.3228 | 1.92 | 75 | 0.3424 | 0.6386 | |... | 4e4f8aa96c53cbef8f29bcff9b66bf57 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples-new 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: 0.3103 - Accuracy: 0.8667 - F1: 0.8667 | 1b98f96ff37316b096009523f417cdfc |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.