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 | ['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_224.in1k', pretrained=True) model = mod... | 143da3bbf2b8a64c5b0b12e4b40f2cab |
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_224.in1k', pretrained=True, ... | e843104794951bde9fa012d073419178 |
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_224.in1k', pretrained=True, nu... | 38eb23f8d3887cb220594012a240aac5 |
mit | ['generated_from_trainer'] | false | gpt2_tryout This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2275 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9182 | 15c905c553e756993facb2f5133a27ce |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 174 | 0.2725 | 0.0 | 0.0 | 0.0 | 0.9019 | | No log | 2.0 | 348 | 0... | 0c3beb669cc1729053a8d0f7941cc1dd |
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: 0.2205 - Accuracy: 0.936 - F1: 0.9361 | 74cce76deece38cf74dab96198ca5c25 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0442 | 1.0 | 250 | 0.2392 | 0.926 | 0.9265 | | 0.0463 | 2.0 | 500 | 0.2205 | 0.936 | 0.9361 | | d639169c5c51576c26bcce2f02e87c55 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-rahuldave-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2505 | 72e764e9d7942c2f9d7a2c7688efa0c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1019 | 1.0 | 291 | 1.6982 | | 1.6376 | 2.0 | 582 | 1.4442 | | 1.4815 | 3.0 | 873 | 1.3822 | | 1.3996 | 4.0 | 1164 | 1.3695 ... | 876d62a85a5498af5ab709fc3f88860f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 345 | 0.8173 | 52.0119 | 27.6158 | 44.7895 | 44.8584 | 16... | 8355cc266d9c374dbaaa32f7e6dbb600 |
other | ['computer_vision', 'pose_estimation'] | false | Copyright 2021-2023 by Mackenzie Mathis, Alexander Mathis, Shaokai Ye and contributors. All rights reserved. - Non-commercial use only is permitted - please cite Ye et al if you use this model in your work https://arxiv.org/abs/2203.07436v1 - If this license is not suitable for your business or project please conta... | f6bdadd352cb5dbe2a8d6d670b5108f5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm500_aug5 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: 0.8927 - Mse: 2.9755 - Mae: 1.0176 - R2: 0.4184 - Accuracy: 0.50... | c78c4b539cefdd4537be5d836174cc57 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.4176 | 1.0 | 3952 | 1.0499 | 3.4996 | 1.0853 | 0.3160 | 0.4593 | | 0.3196 | 2.0 | 7904 ... | 2b7bdccd36ab06a5642df5e8422496ea |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-sst2-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4704 - Accuracy: 0.7792 | ac17036b0413738a77534a2c7de9aa74 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6131 | 0.15 | 500 | 0.5383 | 0.7337 | | 0.5434 | 0.31 | 1000 | 0.5325 | 0.7393 | | 0.5218 | 0.46 | 1500 | 0.4985 | 0.... | 57e31328bb3d4a758218c75f1ba03f57 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'headline-generation'] | false | mT5 Base for News Headline Generation 📣 🇮🇹 This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on news headline generation on the Italian HeadGen-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian... | 82356dbda39ffabaebc9e4f3f4e61722 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'headline-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines hg = pipeline("text2text-generation", model='it5/mt5-base-headline-generation') hg("Arriva dal Partito nazionalista basco (Pnv) la confe... | 470a2cc9080aa8b240a113b9def7b8f0 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Classical | fe4217b99bba08a3c2dbcf91416e57af |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Usage ```python from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("StatsGary/audio-diffusion-hiphop-classical") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) ``` | 1bd0d3474390aeeae048ce4cbff2c26f |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-qnli 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: 0.3458 - Accuracy: 0.8583 | 9dbdbe904e9c25aee29c710e9a921d49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.488 | 0.15 | 500 | 0.3901 | 0.8316 | | 0.4449 | 0.31 | 1000 | 0.3826 | 0.8373 | | 0.4243 | 0.46 | 1500 | 0.3596 | 0.... | 394fb2cf2028a397918dd6011e88bb36 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.0001_pretrainedTrue_epochs3 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0503 | 301cf8821a9591eae7092634b63b0b66 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2809 | 1.0 | 1322 | 2.8898 | | 1.9683 | 2.0 | 2644 | 2.9770 | | 1.8548 | 3.0 | 3966 | 3.0503 | | 6097dca504c7f6da0a3c803de63018ae |
apache-2.0 | ['translation'] | false | opus-mt-fi-bem * source languages: fi * target languages: bem * OPUS readme: [fi-bem](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-bem/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 91be9911fe3ece6d9e1a98283a439d06 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-fr_s458 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 6a5c8cfc1defc086a1e14920edf9e32e |
mit | [] | false | Garfield-Pizza-Plush-v2 on Stable Diffusion This is the `<garfield-plushy>` 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) note... | 04280a2c6343cc061713daaf56984d34 |
apache-2.0 | ['generated_from_keras_callback'] | false | mn367/mark-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0868 - Validation Loss: 2.7662 - Epoch: 0 | 934cdbbaf32709e3ad1a07298054e50d |
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... | 748ce878224216c6c19467a9dadb4c0c |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-kaggglenews This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6240 - Rouge1: 28.3618 - Rouge2: 15.9828 - Rougel: 24.078 - Rougelsum: 25.565 - Gen Len: 20.0 | 1716c16374260ec83325e39cf3f98ecf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 1.9433 | 1.0 | 989 | 1.6240 | 28.3618 | 15.9828 | 24.078 | 25.565 | 20.0 ... | decff192b30846808aa2adf982f19dc5 |
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: 0.7788 - Accuracy: 0.9155 | 13dcc713d8e1c54f38b12bcc5e0368d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2841 | 1.0 | 318 | 3.2794 | 0.7465 | | 2.623 | 2.0 | 636 | 1.8719 | 0.8335 | | 1.5474 | 3.0 | 954 | 1.1629 | 0.... | 645833e362ab00c3cdc76db2eceb761b |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-5_female-5_s336 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 t... | 947c83f36be35bbeae4674fcaaefd8a6 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech_s42 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | d8c1af1e970170d5a6e6ad64873accef |
CC-BY-SA-4.0 | ['spacy', 'token-classification'] | false | Japanese transformer pipeline (bert-base). Components: transformer, parser, ner. | Feature | Description | | --- | --- | | **Name** | `ja_gsd_bert_wwm_unidic_lite` | | **Version** | `3.1.1` | | **spaCy** | `>=3.1.0,<3.2.0` | | **Default Pipeline** | `transformer`, `parser`, `ner` | | **Components** | `transformer`, `p... | 288e520979e139fc425dc57d3f315892 |
CC-BY-SA-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (45 labels for 2 components)</summary> | Component | Labels | | --- | --- | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `... | 416efbe0b7b55fa4029d2d635306ddf6 |
CC-BY-SA-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `DEP_UAS` | 93.68 | | `DEP_LAS` | 92.61 | | `SENTS_P` | 92.02 | | `SENTS_R` | 95.46 | | `SENTS_F` | 93.71 | | `ENTS_F` | 84.04 | | `ENTS_P` | 84.96 | | `ENTS_R` | 83.14 | | `TAG_ACC` | 0.00 | | `TRANSFORMER_LOSS` | 28861.67 | | `PARSER_LOSS` | 1306248.63 | | `NER_LOSS` | 1399... | 78a3f603ac099ad0403abf0ef365cc6b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1455 | 0953026963037b4b98550ca73be97569 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2056 | 1.0 | 5533 | 1.1415 | | 0.949 | 2.0 | 11066 | 1.1144 | | 0.7471 | 3.0 | 16599 | 1.1455 | | a3769b27d7c1257af8b3d6981e1d7167 |
cc-by-4.0 | [] | false | StableDiffusion 1.5 finetuned with the Gatewatch members. vectors trained: - nissarevane  - chandranalaar  ekman_labels = ekman("Thanks for using it.") print(ekman_labels) ``` | 9c4bd3caf6ae097e2114be8e8437ef07 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-cnn-wei1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.6819 - Rouge1: 41.1796 - Rouge2: 18.9426 - Rougel: 29.2338 - Rougelsum: 38.4087 - Gen Len: 72.7607 | ec65d67c7f563ee6e48996f454a2726a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.8582 | 1.0 | 23927 | 1.6819 | 41.1796 | 18.9426 | 29.2338 | 38.4087 |... | 862cc6edff7624c7fb7df5a8e6c8767a |
mit | ['generated_from_keras_callback'] | false | Deep98/Paper-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4183 - Train End Logits Accuracy: 0.8611 - Train Start Logits Accuracy: 0.8... | e17081c445dd796d90f02e7236060c92 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 6cb05e590fb4f52b1e7f3e49b4cc91cf |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_cola_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6624 - Matthews Correlation: 0.0618 | 8c0c5307e3c7f7712faa5ee1f66347e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.5456 | 1.0 | 1669 | 0.6624 | 0.0618 | | 0.4572 | 2.0 | 3338 | 0.7774 | 0.0514 | |... | d1d302644922a96eb9c9297b2a39caf8 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-100k_s630 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-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... | 106192a26b48e0765578781e701f7d38 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'dv', 'robust-speech-event', 'model_for_talk'] | false | wav2vec2-xls-r-1b-dv This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1702 - Wer: 0.2123 | 64f1e456156667e0ddd8620375cc28f4 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'dv', 'robust-speech-event', 'model_for_talk'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | ae7100cfdec23af31a4607f278ece767 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'dv', 'robust-speech-event', 'model_for_talk'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.8412 | 0.66 | 400 | 0.7160 | 0.7913 | | 0.6832 | 1.33 | 800 | 0.3401 | 0.5268 | | 0.4624 | 1.99 | 1200 | 0.2671 | 0.468... | 0aee2183e0ae23839f29494e50a037d9 |
mit | [] | false | Spritual monsters on Stable Diffusion This is the `<spritual-monsters>` 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... | 03785baf28f12c465bb9e9e84cab8051 |
mit | [] | false | Model Description A CLIP ViT-B/32 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. | 8161ef0de6ceed7088070068cfd79df2 |
mit | [] | false | Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/B-32-2B--VmlldzoyNDkwNDMy). | fcd4dbdf8e40ac2df7267318b82b0f4b |
mit | [] | false | Results The model achieves a 66.6 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. | 5102a4a1a08780c1e6299aceccfe08de |
mit | [] | false | Citation **BibTeX:** In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite: OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A... | 361727e5e87dbf5641b5d893a8bfce23 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner-kmeans-twitter This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Precision: 0.6885 - Recall: 0.7665 - F1: 0.7254 | 5cc593cdd8b1676b404a1d0a59393acc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 | | No log | 2.0 | 490 | 0.2744 | 0.6308 ... | 4000a0c075cae174c9e283a63d30bfca |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-unidic) and [UD_Japanese-GSDLUW](https://github.com/UniversalDepen... | 7a2fa0d873367f3afb2224e1f24dc781 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-unidic-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-large-japanese-unidic-ud-head") qap=... | 1204a9c0064c0ea3a4cf0240bd689ec7 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersSl... | 625aaf4bb0a116f1b1ecdc8ee5988ff8 |
apache-2.0 | ['translation'] | false | opus-mt-iso-fr * source languages: iso * target languages: fr * OPUS readme: [iso-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/iso-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 03214cc1af80efcaa7a79e1a57f2c241 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-hu-en Neural machine translation model for translating from Hungarian (hu) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | 1514225e05b7163670fd3f14f2f1b825 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-09 * source language(s): hun * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](htt... | c8c5e5352fe7c2044b7bcbbc58467584 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Bárcsak ne láttam volna ilyen borzalmas filmet!", "Iskolában van." ] model_name = "pytorch-models/opus-mt-tc-big-hu-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.f... | 20612beca46f97b582df7b8c6c04f41b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | She's at school. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-hu-en") print(pipe("Bárcsak ne láttam volna ilyen borzalmas filmet!")) | 42570bd297a6321846d054a8278173b6 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 8eb1fe2c1630124f830a050d9a94d442 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | hun-eng | tatoeba-test-v2021-08-07 | 0.66644 | 50.4 | 13037 | 94699 | | hun-eng | flores101-devtest | 0.61974 | 34.6 | 1012 | 24721 | | hun-eng | newssyscomb2009 | 0.52563 | 24.7 | 502 | 11818 | | hun-eng | newstest2009 | 0.51698 | 23.4 | 2525 | 65399 | ... | 17948c7b4a65412da3390c0d15194025 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset. | d7e009eebe36b6fefa79448093fe5bc6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-libri-train100-colab This model is a fine-tuned version of [GW12/wav2vec2-base-timit-demo-colab](https://huggingface.co/GW12/wav2vec2-base-timit-demo-colab) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2039 - Wer: 0.1190 | 890c3a578ecd690652f5d0c2d65443ca |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_tr... | d5988362d791d7e333028611802fd0b2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.9399 | 0.18 | 500 | 0.3129 | 0.2584 | | 0.2556 | 0.36 | 1000 | 0.7132 | 0.2435 | | 0.2184 | 0.54 | 1500 | 0.4794 | 0.238... | 19bebd32a59c21d1bb5476558829c56e |
mit | [] | false | model by Worldwars This your the Stable Diffusion model fine-tuned the yingdream concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of an anime girl** You can also train your own concepts and upload them to the library by using [this notebook](https://cola... | cf251665b5d9dd3e451ff9576c5de2ba |
mit | [] | false | Happy_Person12345_Assets on Stable Diffusion This is the `<Happy-Person12345-assets>` 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.i... | 62b2d5fe6394491ac37c9e0d671f962a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-small-Assamese This model is a fine-tuned version of [kpriyanshu256/whisper-small-as-500-64-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-small-as-500-64-1e-05-bn) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5071 - Wer: 32.0159 | 00be9380b28b91770660adad67c5e860 |
apache-2.0 | ['whisper-event', '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 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 4a5a97432cdb0940dfc05eb460ee1fb3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0658 | 8.01 | 100 | 0.3295 | 31.9978 | | 0.0027 | 16.02 | 200 | 0.4516 | 31.8896 | | 0.0005 | 24.02 | 300 | 0.4881 | 31.925... | 7b5e6682d646990c45bca2a6cf4afe96 |
apache-2.0 | ['generated_from_trainer'] | false | model_name-finetuned-alm 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: 1.3002 | 9fc93b995ff4f05b5e7fbb5dfeefa010 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.13 | 1.0 | 2 | 1.3157 | | 1.7507 | 2.0 | 4 | 1.3075 | | 1.2933 | 3.0 | 6 | 1.2200 | | 9ba6ceb6f0ae330b1f2b376f0df52d3e |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_80k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 80k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different r... | fda7806aeb339a5e02697ac6e537ad47 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_80k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_80k') model = TFBertModel.from_pretrained("google/multiber... | ac50fcc662efd97e9d3fb1d50bad7c08 |
apache-2.0 | ['translation'] | false | rus-vie * source group: Russian * target group: Vietnamese * OPUS readme: [rus-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-vie/README.md) * model: transformer-align * source language(s): rus * target language(s): vie * model: transformer-align * pre-processing: normalization + S... | e25943de7c7566a662c606ec3e3aaefd |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-vie - source_languages: rus - target_languages: vie - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-vie/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'vi'] - src_constituents: {'rus'} - tgt_const... | 3b306db9893b7ca2a46d9257f7472adc |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert_oscarth_0060 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1876 - Validation Loss: 1.1378 - Epoch: 59 | d8d9f6a44053ffd188c49a6420566402 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1327 | 2.9983 | 0 | | 2.7813 | 2.4562 | 1 | | 2.4194 | 2.2066 | 2 | | 2.2231 | 2.0562 | 3 | | 2.0894 | 1.9450 | 4 | | 1.9905 |... | 1b6720280a51b0a14092e982d9033944 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ab', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | 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 - AB dataset. It achieves the following results on the evaluation set: - Loss: 0.5620 - Wer: 0.5651 | 945837102e5af219c2d42110694cbef3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ab', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7 --dataset mozilla-foundation/common_voice_7_0 --config ab --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA | 6567c513f0fa8fb2248946d14ea4af00 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ab', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - 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_sc... | 6a9034978c2a95394f1e2253181c542d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ab', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.6445 | 13.64 | 300 | 4.3963 | 1.0 | | 3.6459 | 27.27 | 600 | 3.2267 | 1.0 | | 3.0978 | 40.91 | 900 | 3.0927 | 1.0 | |... | 40a115fd1cf507a90a97b72975469147 |
apache-2.0 | ['farsi', 'persian'] | false | GPT2-Persian bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences: 1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable 2. Instead of BPE, google sentence piece tokenizor is used f... | 40329a8f8ef175fb1490694582c77a30 |
apache-2.0 | ['farsi', 'persian'] | false | How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel tokenizer = AutoTokenizer.from_pretrained('bolbolzaban/gpt2-persian') model = GPT2LMHeadModel.from_pretrained('bolbolzaban/gpt2-persian') generator = pipeline('te... | 54fc5972502c87f729f53765aa2d536b |
apache-2.0 | ['farsi', 'persian'] | false | Special Tokens gpt-persian is trained for the purpose of research on Persian poetry. Because of that all english words and numbers are replaced with special tokens and only standard Persian alphabet is used as part of input text. Here is one example: Original text: اگر آیفون یا آیپد شما دارای سیستم عامل iOS 14.3 یا i... | 5ecc1f080e63df1e5fb758fb0055234d |
apache-2.0 | ['farsi', 'persian'] | false | Contacts Please reachout on [Linkedin](https://www.linkedin.com/in/khashei/) or [Telegram](https://t.me/khasheia) if you have any question or need any help to use the model. Follow [Bolbolzaban](http://bolbolzaban.com/about) on [Twitter](https://twitter.com/bolbol_zaban), [Telegram](https://t.me/bolbol_zaban) or [Ins... | b80260e49a6f45a0efd93300953c040a |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/distilbert-base-uncased-mlm-electronics 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: 2.1782 - Validation Loss: 2.0887 - Epoch: 2 | aca4181e8a49c614d3248279582a8ce0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.3455 | 2.2411 | 0 | | 2.2561 | 2.1496 | 1 | | 2.1782 | 2.0887 | 2 | | 31ef06c82d11795e711ef5fb60554a62 |
apache-2.0 | [] | false | Introduction The research for social science texts needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of ... | 4ead16a670213b68cba6b40f83b1d2a2 |
apache-2.0 | [] | false | Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain SSCI-BERT and SSCI-SciBERT models online. - SSCI-BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained(... | b4fc5b731bdd47ef9bf922852f173528 |
apache-2.0 | [] | false | From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/SSCI-BERT-e2](https://huggingface.co/KM4STfulltext/SSCI-BERT-e2) - [KM4STfulltext/SSCI-SciBERT-e2](https://huggingface.co/KM4STfulltext/SSCI-SciBERT-e2) - [KM4STfulltext/SSCI-BERT-e4 ](https://huggingface.co/KM4STfulltext... | 45a61be599790e40414b8b702c30166b |
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