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 | [] | false | Introduction Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances. In this paper, we propose ERNIE-Layout, a nov... | 4b7b9614f5c0d2beace2fb7cda24e0da |
apache-2.0 | [] | false | Citation Info ```text @article{ernie2.0, title = {ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding}, author = {Peng, Qiming and Pan, Yinxu and Wang, Wenjin and Luo, Bin and Zhang, Zhenyu and Huang, Zhengjie and Hu, Teng and Yin, Weichong and Chen, Yongfeng and Zhang, Y... | 9cffa53164b80ec67e933f3f44e83766 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3179 - Accuracy: 0.8733 - F1: 0.8742 | 07abdf17e37fbdf1cb05e6810b5defc0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-uncased-massive-intent-classification This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.8396 - Accuracy: 0.8854 | d733139bdb42fc6842a57a682666a49d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.4984 | 1.0 | 720 | 0.6402 | 0.8495 | | 0.4376 | 2.0 | 1440 | 0.5394 | 0.8731 | | 0.2318 | 3.0 | 2160 | 0.5903 ... | 34f6e1e79f78eef210935ed2adc73d8d |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-uncased-label_vector 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: 1.5258 - F1: 0.2606 | 1b7d404078ccfa56825eeb370a38c536 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1324 | 1.18 | 100 | 1.9573 | 0.0997 | | 1.8322 | 2.35 | 200 | 1.8104 | 0.1286 | | 1.6653 | 3.53 | 300 | 1.7238 | 0.1577 | |... | 91eb69cbbbc1c5abb7dcdf51b6c822ad |
apache-2.0 | ['generated_from_trainer'] | false | stack-overflow-open-status-classifier-pt This model is a fine-tuned version of [reubenjohn/stack-overflow-open-status-classifier-pt](https://huggingface.co/reubenjohn/stack-overflow-open-status-classifier-pt) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9448 - eval_runt... | 641a3e76810349a46fe46e826e25e85f |
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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 1 | de00c92a95b8bf9ef0fe9ff639c1e732 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-subjqa-vanilla-tripadvisor-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](ht... | f8bfee9b330c1efd742c20eaa82179eb |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (tripadvisor) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.co... | 994b3fdf4cca2d3f138ac7dc80751037 |
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/bart-large-subjqa... | aed0be014a7ebb87a707834e7da33998 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-subjqa-vanilla-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset ... | c5f240291b4af76ed8dc2f1a4959e80e |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: tripadvisor - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoc... | 790825048e51060f39779e115e0182d0 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | Whisper Medium Danish (CV11 + FLEAURS) This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs da,da_dk dataset. It achieves the following results on the evaluation set: - Loss: 0.5814 - Wer: 13.7086 | 25cf7846227230a10cc0c5853bda900d |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precis... | e75e78e871fd13c68699919c8e1608a9 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0265 | 3.14 | 1000 | 0.3690 | 14.7607 | | 0.0063 | 6.29 | 2000 | 0.4342 | 14.0926 | | 0.0016 | 9.43 | 3000 | 0.4847 | 1... | 23ba42d341420dcf34572b1e7ef2e1dd |
apache-2.0 | ['dialogue-summarization'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - 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_ratio: 0.1 - num_epochs: 50.0 - label_smoothing_fac... | 4671404f525fbf34630baacada9675c5 |
apache-2.0 | ['dialogue-summarization'] | false | Results on Test Set - predict_gen_len = 329.2 - predict_rouge1 = **48.7673** - predict_rouge2 = **18.1832** - predict_rougeL = **26.1713** - predict_rougeLsum = **46.8434** - predict_samples = 20 - predict_samples_per_second =... | 10429d15dc311477eec7d0919b26af94 |
apache-2.0 | ['generated_from_keras_callback'] | false | philschmid/vit-base-patch16-224-in21k-euroSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0218 - Train Accuracy: 0.9990 - Train Top-3-... | 48ce6fbc93a479ef06978f48103bcd8e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3585, 'end_learning_ra... | 2dea9521fc39532c182b67c659f0853c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 0.4692 | 0.9471 | 0.9878 ... | e618d15f07a04a017712a344f3309083 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1348 - Pearson: nan - Spearmanr: nan - Combined Score: nan | 93ab52a72cf97b7114fda4cf14c225c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 3.4305 | 1.0 | 23 | 2.1402 | -0.0344 | -0.0359 | -0.0352 | | 2.3785 | 2.0 | 46 ... | dc4dedbcb909e6aa7c233bd4dac749ae |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | AltDiffusion | 名称 Name | 任务 Task | 语言 Language(s) | 模型 Model | Github | |:----------:| :----: |:-------------------:| :----: |:------:| | AltDiffusion-m9 | 多模态 Multimodal | Multilingual | Stable Diffusion | [FlagAI](https://github.com/FlagAI-Open/FlagAI) | | 9d7fe10a5d033465d54b4534e13a905d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run AltDiffusion-m9: [', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | 模型信息 Model Information 我们使用 [AltCLIP-m9](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md)，基于 [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) 训练了双语Diffusion模型，训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LAION](https://huggingface.co/datasets/... | 37923e4974ee3a94dfb71a259172bac7 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | 引用 关于AltCLIP-m9，我们已经推出了相关报告，有更多细节可以查阅，如对您的工作有帮助，欢迎引用。 If you find this work helpful, please consider to cite ``` @article{https://doi.org/10.48550/arxiv.2211.06679, doi = {10.48550/ARXIV.2211.06679}, url = {https://arxiv.org/abs/2211.06679}, author = {Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulo... | 255c801873b44b5b1b2078595b572089 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | 模型权重 Model Weights 第一次运行AltDiffusion-m9模型时会自动从huggingface下载如下权重, The following weights are automatically downloaded from HF when the AltDiffusion-m9 model is run for the first time: | 模型名称 Model name | 大小 Size | 描述 Description | |------------------------------|... | 419627680ed668cabfa46fe23f9dfc79 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | scrollTo=1TrIQp9N1Bnm)已放到colab上，欢迎使用。 您可以在 [此处](https://huggingface.co/docs/diffusers/main/en/api/pipelines/alt_diffusion) 查看文档页面。 以下示例将使用fast DPM 调度程序生成图像, 在V100 上耗时大约为 2 秒。 You can run our diffusers example through [here](https://colab.research.google.com/drive/1htPovT5YNutl2i31mIYrOzlIgGLm06IX | 5d02c217d4e88704caee281f567f0f28 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | scrollTo=1TrIQp9N1Bnm) in colab. You can see the documentation page [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/alt_diffusion). The following example will use the fast DPM scheduler to generate an image in ca. 2 seconds on a V100. First you should install diffusers main branch and some depende... | c0b452ee0baba0a94ffea2ecb705806b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | prompt = "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap" image = pipe(prompt, num_inference_steps=25).images[0] image.save("./... | 688f4bb07d67e6c7c1a8ad787c1d442f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | Transformers Example ```python import os import torch import transformers from transformers import BertPreTrainedModel from transformers.models.clip.modeling_clip import CLIPPreTrainedModel from transformers.models.xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer from diffusers.schedulers import DDIMSc... | 90b7cf7a50564d4884b7888a76c74ccc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | self.learn_encoder = learn_encoder class RobertaSeriesModelWithTransformation(BertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] base_model_prefix = 'roberta' config_class= XLMRobertaConfig def __i... | e516591b57c44d530f1f8afcdf4d09e3 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | prompt:dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap | d51cbec53f07c5767714cf185d7009cc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | Ours:  注: 此处长图生成技术由右脑科技(RightBrain AI)提供。 Note: The long image generation technology here is provided by Right Brain Technology. | d1f2b90c0f8682b45ebad17eb8058759 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | 许可/License 该模型通过 [CreativeML Open RAIL-M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) 获得许可。作者对您生成的输出不主张任何权利，您可以自由使用它们并对它们的使用负责，不得违反本许可中的规定。该许可证禁止您分享任何违反任何法律、对他人造成伤害、传播任何可能造成伤害的个人信息、传播错误信息和针对弱势群体的任何内容。您可以出于商业目的修改和使用模型，但必须包含相同使用限制的副本。有关限制的完整列表，请[阅读许可证](https://huggingface.co/spaces/CompVis/s... | 98483acac2356e603c8dbdf45f1e683f |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-fi-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset. It achieves the following results on the evaluation set: - Loss: 3.3598 - Bleu: 1.618 - Gen Len: 17.3223 | 84d5dc2991d87829602b622d7fbcc8d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 3.3627 | 1.0 | 6250 | 3.5122 | 1.2882 | 17.1803 | | 3.2162 | 2.0 | 12500 | 3.4442 | 1.4329 | 17.2617 | | 3.1304 |... | 695f1ff63fe16befc72aaeaa60791ca6 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'pt', 'robust-speech-event'] | false | sew-tiny-portuguese-cv8 This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4082 - Wer: 0.3053 | d908aae5f19ddddc146a8355c5e0c924 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'pt', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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_sche... | 3fda5e1039f035a97403866f3a7de380 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'pt', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 1.93 | 1000 | 2.9134 | 0.9767 | | 2.9224 | 3.86 | 2000 | 2.8405 | 0.9789 | | 2.9224 | 5.79 | 3000 | 2.8094 | 0.980... | c4706c8316f2503cbdaf6d9c62d02d33 |
apache-2.0 | ['generated_from_keras_callback'] | false | DistBERT_ideology 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: | c20d68a4fa57ea9f6195069a5868a3e7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 5f360ec7b030b5965437b319718d134f |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.5993 - Rouge1: 15.1689 - Rouge2: 2.1762 - Rougel: 12.7542 - Rougelsum: 14.0214 - Gen Len: 18.9988 | e403cec95f90ab0b2d79eedcbc202fd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.8011 | 1.0 | 17040 | 3.5993 | 15.1689 | 2.1762 | 12.7542 | 14.0214 | 18... | e874e80184160d42e38082d061b587b6 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation [Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentation ... | 3df2ed2be9bc3b63684ff6ed8bb1b505 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Use this model ```python from transformers import AutoTokenizer, Wav2Vec2ForCTC tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian") model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Com... | e331e4a7f1e4adfb5c78b968d4930706 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Example test with Common Voice Dataset ```python dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"... | 23dbf4c5f72a228e974c38511840b9f8 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-b0.04 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8124 - Bleu: 7.5994 - Gen Len: 44.6753 | cfd4ec7267717267fbe65f45c1c173b5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_3_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8908 - F1: 0.7879 | 57b7ac0dab5ceeb4e148f96a2e79296f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5873 | 0.7636 | | 0.5479 | 2.0 | 578 | 0.5788 | 0.7697 | | 0.5479 | 3.0 | 867 | 0.6286 | 0.7770 | |... | d79d748def11278d4180d8060254dd79 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-gradient-clinic 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: 0.2601 | a5506b2a3294d9c3fa386b50fa9312c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 36 - eval_batch_size: 36 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 90159d0132e22ae66189d51d244c3be9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 24 | 0.8576 | | No log | 2.0 | 48 | 0.3439 | | No log | 3.0 | 72 | 0.2807 | | No log | 4.0 | 96 | 0.2653 ... | 2dfe9c670a292f0f0f0826700b01d4ba |
apache-2.0 | ['generated_from_trainer'] | false | hf_fine_tune_hello_world This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.6084 - Accuracy: 0.205 | fd96ed4ebee15b434b1cddf70254a009 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.6245 | 0.22 | | No log | 2.0 | 250 | 1.6120 | 0.205 | | No log | 3.0 | 375 | 1.6084 | 0.... | f606deadc9530bcece23208459def93c |
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: 0.7784 - Matthews Correlation: 0.5499 | 3002104a58a632cae001c3f96efcf3c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5248 | 1.0 | 535 | 0.5367 | 0.4142 | | 0.3488 | 2.0 | 1070 | 0.5116 | 0.5083 | | 0.2... | cf193ae7b7586f2c0b0b100249977459 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-100k_s468 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_7_0). When using this model, make sure th... | b755c98825213e1e7b0a2add4cb19567 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | BertSpan for Chinese Named Entity Recognition(bertspan4ner) Model 中文实体识别模型 `bertspan4ner-base-chinese` evaluate PEOPLE(人民日报) test data： The overall performance of BertSpan on people **test**: | | Accuracy | Recall | F1 | | ------------ | ------------------ | ------------------ | ------------------... | 0f6dda0b0d8207a4b8d0b811b35c0d5a |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | Usage 本项目开源在实体识别项目：[nerpy](https://github.com/shibing624/nerpy)，可支持bertspan模型，通过如下命令调用： ```shell >>> from nerpy import NERModel >>> model = NERModel("bertspan", "shibing624/bertspan4ner-base-chinese") >>> predictions, raw_outputs, entities = model.predict(["常建良，男，1963年出生，工科学士，高级工程师"], split_on_space=False) entities:... | cd75546ac3ce1d9ff0009b0d9de6e182 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | 中文实体识别数据集 | 数据集 | 语料 | 下载链接 | 文件大小 | | :------- | :--------- | :---------: | :---------: | | **`CNER中文实体识别数据集`** | CNER(12万字) | [CNER github](https://github.com/shibing624/nerpy/tree/main/examples/data/cner)| 1.1MB | | **`PEOPLE中文实体识别数据集`** | 人民日报数据集（200万字） | [PEOPLE github](https://github.com/shibing624/nerpy/tree/... | c6f6e45917e5ad5213441b5ce648c7b5 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | deployment-with-nvidia-riva) | This model transcribes speech in lower case English alphabet along with spaces and apostrophes. It is an "large" versions of Citrinet-CTC (around 140M parameters) model. See the [model architecture]( | 3b8e19aa0fa37798907675bd1337e51c |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_1024_ls" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | d7485ea7bd3aea83e40f7f6ab05aa8b6 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | |---------|---------------------------|-----... | d0ee8d6de3bdfb096d6dbae9ee8804cf |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | mk_core_news_md Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `mk_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `morphologizer`, `pa... | 42fba7dda554e7325a6bd0777efe0710 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (54 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=PROPN`, `POS=AUX`, `POS=ADJ`, `POS=NOUN`, `POS=ADP`, `POS=PUNCT`, `POS=CONJ`, `POS=NUM`, `POS=VERB`, `POS=PRON`, `POS=ADV`, `POS=SCONJ`, `POS=PART`, `POS=SYM`, `... | 74b96641eda33bbb926ba7a30d6b13f9 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_F` | 100.00 | | `SENTS_P` | 80.00 | | `SENTS_R` | 67.53 | | `SENTS_F` | 73.24 | | `DEP_UAS` | 67.71 | | `DEP_LAS` | 52.01 | | `ENTS_P` | 74.72 | | `ENTS_R` | 74.47 | | `ENTS_F` | 74.60 | | `POS_ACC`... | 806a6b5230af3adabf15b41633a8e1cf |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finetuned-ner-to-multilabel-xglue-ner 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: 0.0616 | 6f3ca7ac858d6133413c252e7df52f10 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: constant - num_epochs: 20 | ba533c3c12e1941d177a850afb340712 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2168 | 0.28 | 500 | 0.1212 | | 0.1067 | 0.57 | 1000 | 0.0865 | | 0.0878 | 0.85 | 1500 | 0.0710 | | 0.0667 | 1.14 | 2000 | 0.0670 ... | 8d0dfa86de2b3866a5f1ef6900d25511 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Italic languages (itc) to Arabic (ar). 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... | 324c8e4899efab806d1f489ab5f22757 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ary<< Entiendo.", ">>arq<< Por favor entiende mi posición." ] model_name = "pytorch-models/opus-mt-tc-big-itc-ar" tokenizer = MarianTokenizer.from_pretrained(model_name)... | bc04fe02f1fa55b3a1f2427c4f7121cb |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | من فضلك افهم موقفي. ``` 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-itc-ar") print(pipe(">>ary<< Entiendo.")) | 1a3c3025c655f1b4023bf6c28243ab0e |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-a... | aefe4f627bde78a6d3a9a377f885332c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-ara/opusTCv20210807_transformer-big_2022-08-09.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/it... | 3ce0fdfda6a3d8e8319186c31fac6015 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | fra-ara | tatoeba-test-v2021-08-07 | 0.46463 | 18.9 | 1569 | 7956 | | ita-ara | tatoeba-test-v2021-08-07 | 0.53797 | 25.7 | 235 | 1161 | | spa-ara | tatoeba-test-v2021-08-07 | 0.55520 | 26.6 | 1511 | 7547 | | cat-ara | flores101-devtest | 0.52029 | 18.9 ... | c2d06ad5d23c028fefdd09390ba90109 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05 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: 0.4095 - Accuracy:... | ee3a5db5c12581a103f1923b70f69716 |
apache-2.0 | [] | false | Model description **CAMeLBERT-DA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset. Our fine... | fc74a0138854fa07ad58cdc2d5943464 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'noun', 'score': 0.84596395, 'index': 1, 'word': 'شلون', ... | 536b309b769ed65dc182133ca86cca23 |
apache-2.0 | [] | false | ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.99996364, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9990874, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.99985224, 'index': 5, 'word': ' | e973a3c4f48930c7140e6a4a6fba2c02 |
apache-2.0 | [] | false | ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999683, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. | f88795b030dfb58d70aee1e94bacab91 |
apache-2.0 | ['generated_from_keras_callback'] | false | ytsai25/bert-finetuned-ner-ADR This model is a fine-tuned version of [ytsai25/bert-finetuned-ner](https://huggingface.co/ytsai25/bert-finetuned-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0347 - Validation Loss: 0.0804 - Epoch: 2 | 268203313d5b1e14db9cbeb6c56a6f36 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 669, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 0f9f44b97796de9c734590478895e89e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1307 | 0.0799 | 0 | | 0.0579 | 0.0758 | 1 | | 0.0347 | 0.0804 | 2 | | 17a2c56e940e4ec5ae41200c2eeb18f5 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 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: 0.4064 - Accuracy... | ddcd84411e8f2ac2c2c2b1bf0b958c4c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4163 | 0.8085 | 0.8780 | | No log | 2.0 | 390 | 0.4098 | 0.8268 | 0.8878 | | 0.312 |... | 972d0287a7b0ab2c7cc413848b046ee1 |
mit | ['pyannote', 'pyannote-audio', 'pyannote-audio-pipeline', 'audio', 'voice', 'speech', 'speaker', 'speaker-diarization', 'speaker-change-detection', 'voice-activity-detection', 'overlapped-speech-detection'] | false | Accuracy This pipeline is benchmarked on a growing collection of datasets. Processing is fully automatic: * no manual voice activity detection (as is sometimes the case in the literature) * no manual number of speakers (though it is possible to provide it to the pipeline) * no fine-tuning of the internal models n... | d8545a57dccd8ac73afd018d7fb0667b |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v7_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5083 - Precision: 0.2364 - Recall: 0.1162 - F1: 0.1559 - Accura... | f5552b3f6c39c7133ee8531d29a6ffa6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 26 | 0.5987 | 0.0582 | 0.0029 | 0.0054 | 0.7847 | | No log | 2.0 |... | da17a33e71f2bd1f62c03c02068207d9 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | da_core_news_lg Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `da_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `... | f43b59a507a77e0d6ef1d648bc7ad55c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion... | f6c150a5445f8958c2e5203c6396b914 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.89 | | `TOKEN_P` | 99.78 | | `TOKEN_R` | 99.75 | | `TOKEN_F` | 99.76 | | `POS_ACC` | 96.66 | | `MORPH_ACC` | 95.74 | | `MORPH_MICRO_P` | 97.43 | | `MORPH_MICRO_R` | 96.75 | | `MORPH_MICRO_F` | 97.09 | | `SENTS_P` | 89.09 | | `SENTS_R` | 88.30 | | `SENTS_F` | ... | 85a6d883ac9ebe34d57f6d4746be3c6a |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-herblabels This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4823 - Rouge1: 3.0759 - Rouge2: 1.0495 - Rougel: 3.0758 - Rougelsum: 3.0431 - Gen Len: 18.9716 | 5daf22102e38b4d319a00f611b009bae |
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 | 264 | 1.6010 | 2.4276 | 0.5658 | 2.3546 | 2.3099 | 18.9091 | |... | 3405af088945542fea5866aa559d3cce |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sur... | 55a3222429ec0ef0c56ffafa19d03636 |
apache-2.0 | ['bert', 'mnli', 'ax', 'glue', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as thos... | 3d3b10112bb3d5c870fa4c744e8e2db1 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-food101-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.5493 - Accuracy: 0.8539 | 7656cee38218e86ed7d1019c9d4eeaa9 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.657 | 1.0 | 4735 | 0.9732 | 0.7459 | | 0.9869 | 2.0 | 9470 | 0.7987 | 0.7884 | | 0.71 | 3.0 | 14205 | 0.6364 ... | aa57e5c1c7e7a747ed586b89525cb1d8 |
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