license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | Training data This model was distiled with 522MB of indonesian Wikipedia and 1GB of [indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018). The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ```[CLS] Sentence A [SEP] Sentence B [SEP]``` | 139d9a9f410fe0fcab87e1213e93142a |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-devices-sum-ver1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0935 - Rouge1: 97.2294 - Rouge2: 80.1323 - Rougel: 97.245 - Rougelsum: 97.2763 - Gen Len: 4.9507 | 7997838a9cc7c0b4fd948be88d5c615a |
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 | 186 | 0.2461 | 91.9436 | 71.232 | 91.9417 | 91.9585 | 4.6644 | | No log | 2.0 | 372 | 0.1580 | 94.5247 | 76.1321 | 94.5044 | 94.5382 | 4.8953 | | 0.488 | 3.0 | 558 | 0.1239 | 95.8673 | 78.1183 | 95.8862 | 95.8919 | 4.9102 | | 0.488 | 4.0 | 744 | 0.1100 | 96.5746 | 78.9878 | 96.5848 | 96.5831 | 4.9102 | | 0.488 | 5.0 | 930 | 0.1008 | 96.9074 | 79.5536 | 96.9143 | 96.9317 | 4.9291 | | 0.1303 | 6.0 | 1116 | 0.0974 | 96.9274 | 79.6953 | 96.933 | 96.9473 | 4.9291 | | 0.1303 | 7.0 | 1302 | 0.0969 | 96.8041 | 79.5073 | 96.817 | 96.8266 | 4.9271 | | 0.1303 | 8.0 | 1488 | 0.0945 | 97.1496 | 79.9757 | 97.1529 | 97.1779 | 4.9534 | | 0.089 | 9.0 | 1674 | 0.0944 | 97.253 | 80.1236 | 97.2619 | 97.2899 | 4.9595 | | 0.089 | 10.0 | 1860 | 0.0935 | 97.2294 | 80.1323 | 97.245 | 97.2763 | 4.9507 | | 8e9442b2f99dbdf9e8b4aa5d6d44d326 |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | CLIP (RoBERTa) | 326M | 使用了ViT-H作为视觉提取器-中文 ViT-H-Chinese | | 350e80bd01d29b0102f1120557af1818 |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 模型信息 Model Information 我们遵循CLIP的实验设置，以获得强大的视觉-语言表征。在训练中文版的CLIP时，我们使用[chinese-roberta-wwm-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)作为语言的编码器，并将[open_clip](https://github.com/mlfoundations/open_clip)中的**ViT-H-14**应用于视觉的编码器。为了快速且稳定地进行预训练，我们冻结了视觉编码器并且只微调语言编码器。此外，我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集。在悟空数据集和zero数据集上预训练24轮，在A100x32上训练了8天。据我们所知，我们的Taiyi-CLIP是目前Huggingface社区中首个的开源中文CLIP。 We follow the experimental setup of CLIP to obtain powerful visual-language intelligence. To obtain the CLIP for Chinese, we employ [chinese-roberta-wwm-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) for the language encoder, and apply the **ViT-H-14** in [open_clip](https://github.com/mlfoundations/open_clip) for the vision encoder. We freeze the vision encoder and tune the language encoder to speed up and stabilize the pre-training process. Moreover, we apply [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/) dataset (100M) and [Zero](https://zero.so.com/) dataset (23M) as the pre-training datasets. The model was first trained 24 epochs on wukong and zero, which takes 8 days to train on A100x32. To the best of our knowledge, our TaiyiCLIP is currently the only open-sourced Chinese CLIP in the huggingface community. | 5afb009b70476f750db3135b9ecb570a |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 下游效果 Performance **Zero-Shot Classification** | model | dataset | Top1 | Top5 | | ---- | ---- | ---- | ---- | | Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese | ImageNet1k-CN | 54.35% | 80.64% | **Zero-Shot Text-to-Image Retrieval** | model | dataset | Top1 | Top5 | Top10 | | ---- | ---- | ---- | ---- | ---- | | Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese | Flickr30k-CNA-test | 60.82% | 85.00% | 91.04% | | Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese | COCO-CN-test | 60.02% | 83.95% | 93.26% | | Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese | wukong50k | 66.85% | 92.81% | 96.69% | | 19c6baaeafdc9abee45d81947cd7861b |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 使用 Usage ```python3 from PIL import Image import requests import open_clip import torch from transformers import BertModel, BertConfig, BertTokenizer from transformers import CLIPProcessor, CLIPModel import numpy as np query_texts = ["一只猫", "一只狗",'两只猫', '两只老虎','一只老虎'] | 041d3a0b4911c2cd500a220caa9c98c4 |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 加载Taiyi 中文 text encoder text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese") text_encoder = BertModel.from_pretrained("IDEA-CCNL/Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese").eval() url = "http://images.cocodataset.org/val2017/000000039769.jpg" | 4271fe6f8ede017a48d03b91de1996e8 |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 加载openclip的image encoder clip_model, _, processor = open_clip.create_model_and_transforms('ViT-H-14', pretrained='laion2b_s32b_b79k') clip_model = clip_model.eval() text = text_tokenizer(query_texts, return_tensors='pt', padding=True)['input_ids'] image = processor(Image.open(requests.get(url, stream=True).raw)).unsqueeze(0) with torch.no_grad(): image_features = clip_model.encode_image(image) text_features = text_encoder(text)[1] | 042fc9cbfdb11d1762e377b7ea4b0564 |
apache-2.0 | ['clip', 'zh', 'image-text', 'feature-extraction'] | false | 计算余弦相似度 logit_scale是尺度系数 logit_scale = clip_model.logit_scale.exp() logits_per_image = logit_scale * image_features @ text_features.t() logits_per_text = logits_per_image.t() probs = logits_per_image.softmax(dim=-1).cpu().numpy() print(np.around(probs, 3)) ``` | 14e17e5406c252dda3e5e4848db925af |
mit | ['rudalle', 'pokemon', 'image-generation'] | false | ai-generated-pokemon-rudalle  A finetuned [ruDALL-E](https://github.com/sberbank-ai/ru-dalle) on Pokémon using the finetuning example Colab Notebook [linked in that repo](https://colab.research.google.com/drive/1Tb7J4PvvegWOybPfUubl5O7m5I24CBg5?usp=sharing). This model was used to create Pokémon that resulted in AI-Generated Pokémon that went viral ([10k+ retweets](https://twitter.com/minimaxir/status/1470913487085785089) on Twitter + [30k+ upvotes](https://www.reddit.com/r/pokemon/comments/rgmyxp/i_trained_an_ai_on_all_the_official_pokemon/) on Reddit) The model used above was trained for 12 epochs (4.5 hours on a P100), at a max learning rate of `1e-5`. | 8f613378ba14159427fcb4691f03c107 |
other | ['text-generation', 'opt'] | false | How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-iml-1.3b") >>> generator("What is the capital of USA?") ``` | bc9dae0b2c3cb343616b2a3d415b1082 |
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: 1.1572 | 130f5cf5204a211ec01d894ac373577d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 1db8f35d7bc72f16a478f2c458179b9c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2337 | 1.0 | 2767 | 1.1525 | | 0.9515 | 2.0 | 5534 | 1.1206 | | 0.7327 | 3.0 | 8301 | 1.1572 | | c10b45c3a328cb472e380ce8f64e1082 |
apache-2.0 | ['longformer', 'clinical', 'biomedical'] | false | <span style="font-size:larger;">**KEPTlongfomer**</span> is a medical knowledge enhanced version of Longformer that was further pre-trained using [contrastive learning](https://arxiv.org/pdf/2210.03304.pdf). The model achieves SOTA performance on auto ICD coding on MIMIC-III as of 11/12/2022. A sister model for better performance is available [here](https://huggingface.co/whaleloops/KEPTlongformer-PMM3/). | d718b4ebc238b713c5119fe1439e42fd |
apache-2.0 | ['longformer', 'clinical', 'biomedical'] | false | Pre-training We initialized this model from [clinical longformer](https://huggingface.co/yikuan8/Clinical-Longformer). And then pretrained with Hierarchical Self-Alignment Pretrain (HSAP) using Knowledge Graph UMLS. This includes (a) Hierarchy, (b) Synonym, (c) Abbreviation. For more info, see section 3.3 in [paper](https://arxiv.org/pdf/2210.03304.pdf). The learning rate was 5e-5, weight decay was 0.01, adam epsilon was 1e-5. | 5b6abdaa25fdebeec632b332bb5fcaab |
apache-2.0 | ['longformer', 'clinical', 'biomedical'] | false | Usage See our [github](https://github.com/whaleloops/KEPT/tree/rerank300) for how to use this with prompts on auto ICD coding. With the following result: | Metric | Score | | ------------- | ------------- | |rec_micro| =0.5729403619819988| |rec_macro| =0.11342156911120573| |rec_at_8| =0.4094837705486378| |rec_at_75| =0.8470734920535119| |rec_at_50| =0.8005338782352| |rec_at_5| =0.2891628170355805| |rec_at_15| =0.5768778119750537| |prec_micro| =0.6411968713105065| |prec_macro| =0.12227610414493029| |prec_at_8| =0.7760972716488731| |prec_at_75| =0.197504942665085| |prec_at_50| =0.2768090154211151| |prec_at_5| =0.8483392645314354| |prec_at_15| =0.6178529062870699| |f1_micro| =0.6051499904242899| |f1_macro| =0.11768251595637802| |f1_at_8| =0.536107150495997| |f1_at_75| =0.32032290907137506| |f1_at_50| =0.411373195944102| |f1_at_5| =0.43131028155283435| |f1_at_15| =0.5966627077602488| |auc_micro| =0.9651754312635265| |auc_macro| =0.8566590059725866| |acc_micro| =0.43384592341105344| |acc_macro| =0.08639139221100567| | 8b981e76831cb8e37e53e6987d403ced |
mit | ['generated_from_trainer'] | false | finetuning-sentiment-model-tweet-gpt2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3646 - Accuracy: 0.6908 - F1: 0.6908 | ec76ce998debe5e741850ad60c31b889 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 13b75875cabda975ba3d8b9839f9a4af |
apache-2.0 | ['generated_from_keras_callback'] | false | lewtun/distilgpt2-finetuned-shakespeare 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: - Train Loss: 2.9411 - Validation Loss: 3.5767 - Epoch: 29 | f71ada8a6f1c311e301dae868a51ff6b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.2112 | 3.8253 | 0 | | 3.8997 | 3.6898 | 1 | | 3.7783 | 3.6304 | 2 | | 3.7046 | 3.5846 | 3 | | 3.6477 | 3.5667 | 4 | | 3.6001 | 3.5445 | 5 | | 3.5563 | 3.5333 | 6 | | 3.5198 | 3.5240 | 7 | | 3.4842 | 3.5146 | 8 | | 3.4505 | 3.5126 | 9 | | 3.4184 | 3.5022 | 10 | | 3.3912 | 3.5027 | 11 | | 3.3613 | 3.5003 | 12 | | 3.3337 | 3.4985 | 13 | | 3.3045 | 3.5004 | 14 | | 3.2772 | 3.5014 | 15 | | 3.2527 | 3.5018 | 16 | | 3.2274 | 3.5053 | 17 | | 3.2011 | 3.5106 | 18 | | 3.1754 | 3.5143 | 19 | | 3.1512 | 3.5181 | 20 | | 3.1259 | 3.5274 | 21 | | 3.1003 | 3.5215 | 22 | | 3.0809 | 3.5354 | 23 | | 3.0568 | 3.5335 | 24 | | 3.0306 | 3.5502 | 25 | | 3.0080 | 3.5574 | 26 | | 2.9857 | 3.5587 | 27 | | 2.9654 | 3.5760 | 28 | | 2.9411 | 3.5767 | 29 | | b00e449b797c6fac8a5d05e442788f6a |
cc-by-4.0 | ['named-entity-recognition', 'Transformer', 'pytorch', 'bert'] | false | 🤗 bert-restore-punctuation-ptbr * 🪄 [W&B Dashboard](https://wandb.ai/dominguesm/RestorePunctuationPTBR) * ⛭ [GitHub](https://github.com/DominguesM/respunct) This is a [bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for punctuation restoration on [WikiLingua](https://github.com/esdurmus/Wikilingua). This model is intended for direct use as a punctuation restoration model for the general Portuguese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks. Model restores the following punctuations -- **[! ? . , - : ; ' ]** The model also restores the upper-casing of words. ----------------------------------------------- | 3063550767832305584ef905e98cca51 |
cc-by-4.0 | ['named-entity-recognition', 'Transformer', 'pytorch', 'bert'] | false | 🤷 Usage 🇧🇷 easy-to-use package to restore punctuation of portuguese texts. **Below is a quick way to use the template.** 1. First, install the package. ``` pip install respunct ``` 2. Sample python code. ``` python from respunct import RestorePuncts model = RestorePuncts() model.restore_puncts(""" henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes""") | fb7ae4b918407fc569591400caa56cd8 |
cc-by-4.0 | ['named-entity-recognition', 'Transformer', 'pytorch', 'bert'] | false | 🎯 Accuracy | label | precision | recall | f1-score | support| | ------------------------- | -------------|-------- | ----------|--------| | **Upper - OU** | 0.89 | 0.91 | 0.90 | 69376 | **None - OO** | 0.99 | 0.98 | 0.98 | 857659 | **Full stop/period - .O** | 0.86 | 0.93 | 0.89 | 60410 | **Comma - ,O** | 0.85 | 0.83 | 0.84 | 48608 | **Upper + Comma - ,U** | 0.73 | 0.76 | 0.75 | 3521 | **Question - ?O** | 0.68 | 0.78 | 0.73 | 1168 | **Upper + period - .U** | 0.66 | 0.72 | 0.69 | 1884 | **Upper + colon - :U** | 0.59 | 0.63 | 0.61 | 352 | **Colon - :O** | 0.70 | 0.53 | 0.60 | 2420 | **Question Mark - ?U** | 0.50 | 0.56 | 0.53 | 36 | **Upper + Exclam. - !U** | 0.38 | 0.32 | 0.34 | 38 | **Exclamation Mark - !O** | 0.30 | 0.05 | 0.08 | 783 | **Semicolon - ;O** | 0.35 | 0.04 | 0.08 | 1557 | **Apostrophe - 'O** | 0.00 | 0.00 | 0.00 | 3 | **Hyphen - -O** | 0.00 | 0.00 | 0.00 | 3 | | | | | | **accuracy** | | | 0.96 | 1047818 | **macro avg** | 0.57 | 0.54 | 0.54 | 1047818 | **weighted avg** | 0.96 | 0.96 | 0.96 | 1047818 ----------------------------------------------- | 9c0504ff6cbd3a3a14a4ae270d764caf |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5980 - Wer: 0.4949 | 4f9f52b2e227edb309151b69d75369e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2691 | 1.37 | 200 | 2.9045 | 1.0 | | 1.6356 | 2.74 | 400 | 0.9277 | 0.8678 | | 0.8062 | 4.11 | 600 | 0.8200 | 0.7776 | | 0.5983 | 5.48 | 800 | 0.6829 | 0.7161 | | 0.4863 | 6.85 | 1000 | 0.6205 | 0.6507 | | 0.407 | 8.22 | 1200 | 0.6519 | 0.6763 | | 0.3641 | 9.59 | 1400 | 0.5771 | 0.6088 | | 0.3291 | 10.96 | 1600 | 0.6548 | 0.6202 | | 0.2905 | 12.33 | 1800 | 0.6538 | 0.5828 | | 0.2613 | 13.7 | 2000 | 0.6281 | 0.5864 | | 0.2354 | 15.07 | 2200 | 0.5936 | 0.5630 | | 0.2145 | 16.44 | 2400 | 0.5877 | 0.5699 | | 0.2008 | 17.81 | 2600 | 0.5469 | 0.5488 | | 0.1751 | 19.18 | 2800 | 0.6453 | 0.5584 | | 0.169 | 20.55 | 3000 | 0.5871 | 0.5357 | | 0.1521 | 21.92 | 3200 | 0.6063 | 0.5318 | | 0.1426 | 23.29 | 3400 | 0.5609 | 0.5171 | | 0.1287 | 24.66 | 3600 | 0.6056 | 0.5126 | | 0.1236 | 26.03 | 3800 | 0.5994 | 0.5074 | | 0.1138 | 27.4 | 4000 | 0.5980 | 0.4944 | | 0.1083 | 28.77 | 4200 | 0.5980 | 0.4949 | | b40ccb3dc48f7efb0eb723aa85098fec |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody` ♻️ Imported from https://zenodo.org/record/5499066/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | e8dfbcde6054b47f9bcd62ec5cb6d1f1 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_xvector_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4394602/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | cdbdbd981795a64a3dc7291c33d80e92 |
apache-2.0 | [] | false | ByT5 - large ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-large). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4 | ae15bf5cc7bf8cc1881353d615b012be |
apache-2.0 | [] | false | c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-large` significantly outperforms [mt5-large](https://huggingface.co/google/mt5-large) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* | 7c73240a8f6fc324557977703835ce8a |
apache-2.0 | [] | false | Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-large') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 | d0fe6e48f9e18d12a11a09eb917383f1 |
apache-2.0 | [] | false | forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-large') tokenizer = AutoTokenizer.from_pretrained('google/byt5-large') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss | 3fcf14458e7bf4db6d3705f8327ac95b |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the Lila-IID-train/dev set from the [Lila dataset](https://github.com/allenai/Lila). | 9efa78100b50e033313842d4468f9765 |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations If you use this model, please cite our work. ``` @INPROCEEDINGS{Mishra2022Lila, author = { Swaroop Mishra and Matthew Finlayson and Pan Lu and Leonard Tang and Sean Welleck and Chitta Baral and Tanmay Rajpurohit and Oyvind Tafjord and Ashish Sabharwal and Peter Clark and Ashwin Kalyan}, title = {Lila: A Unified Benchmark for Mathematical Reasoning}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022} } ``` | cb6b6cb60ef62d4a4e561bfab0576c8e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 85bf42f463cffe3ccd5792d2c42c4e4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 0.06 | 100 | 0.7930 | 0.8214 | | No log | 0.11 | 200 | 0.7544 | 0.8290 | | No log | 0.17 | 300 | 0.7358 | 0.8328 | | No log | 0.23 | 400 | 0.7192 | 0.8357 | | 0.8156 | 0.28 | 500 | 0.7012 | 0.8397 | | 0.8156 | 0.34 | 600 | 0.6904 | 0.8419 | | 0.8156 | 0.4 | 700 | 0.6802 | 0.8440 | | 0.8156 | 0.45 | 800 | 0.6670 | 0.8465 | | 0.8156 | 0.51 | 900 | 0.6572 | 0.8486 | | 0.7219 | 0.57 | 1000 | 0.6499 | 0.8500 | | 0.7219 | 0.62 | 1100 | 0.6411 | 0.8522 | | 0.7219 | 0.68 | 1200 | 0.6343 | 0.8537 | | 0.7219 | 0.74 | 1300 | 0.6299 | 0.8546 | | 0.7219 | 0.79 | 1400 | 0.6221 | 0.8561 | | 0.662 | 0.85 | 1500 | 0.6157 | 0.8574 | | 0.662 | 0.91 | 1600 | 0.6138 | 0.8579 | | 0.662 | 0.96 | 1700 | 0.6055 | 0.8595 | | 0.662 | 1.02 | 1800 | 0.6143 | 0.8598 | | 0.662 | 1.08 | 1900 | 0.6191 | 0.8599 | | 0.5707 | 1.14 | 2000 | 0.6118 | 0.8607 | | 0.5707 | 1.19 | 2100 | 0.6123 | 0.8611 | | 0.5707 | 1.25 | 2200 | 0.6089 | 0.8617 | | 0.5707 | 1.31 | 2300 | 0.6064 | 0.8619 | | 0.5707 | 1.36 | 2400 | 0.6079 | 0.8625 | | 0.4923 | 1.42 | 2500 | 0.6040 | 0.8625 | | 0.4923 | 1.48 | 2600 | 0.6030 | 0.8630 | | 0.4923 | 1.53 | 2700 | 0.6021 | 0.8636 | | 0.4923 | 1.59 | 2800 | 0.6001 | 0.8643 | | 0.4923 | 1.65 | 2900 | 0.5981 | 0.8644 | | 0.4909 | 1.7 | 3000 | 0.5942 | 0.8648 | | 0.4909 | 1.76 | 3100 | 0.5918 | 0.8650 | | 0.4909 | 1.82 | 3200 | 0.5923 | 0.8659 | | 0.4909 | 1.87 | 3300 | 0.5884 | 0.8664 | | 0.4909 | 1.93 | 3400 | 0.5884 | 0.8663 | | 0.4964 | 1.99 | 3500 | 0.5903 | 0.8669 | | 0.4964 | 2.04 | 3600 | 0.6421 | 0.8655 | | 0.4964 | 2.1 | 3700 | 0.6401 | 0.8651 | | 0.4964 | 2.16 | 3800 | 0.6411 | 0.8649 | | 0.4964 | 2.21 | 3900 | 0.6387 | 0.8645 | | 0.345 | 2.27 | 4000 | 0.6362 | 0.8654 | | 0.345 | 2.33 | 4100 | 0.6362 | 0.8654 | | 0.345 | 2.38 | 4200 | 0.6362 | 0.8654 | | 0.345 | 2.44 | 4300 | 0.6357 | 0.8655 | | 0.345 | 2.5 | 4400 | 0.6362 | 0.8656 | | 0.3463 | 2.55 | 4500 | 0.6377 | 0.8658 | | 0.3463 | 2.61 | 4600 | 0.6357 | 0.8660 | | 0.3463 | 2.67 | 4700 | 0.6294 | 0.8665 | | 0.3463 | 2.72 | 4800 | 0.6333 | 0.8665 | | 0.3463 | 2.78 | 4900 | 0.6362 | 0.8662 | | 0.3508 | 2.84 | 5000 | 0.6357 | 0.8666 | | 0.3508 | 2.89 | 5100 | 0.6299 | 0.8673 | | 0.3508 | 2.95 | 5200 | 0.6313 | 0.8668 | | 0.3508 | 3.01 | 5300 | 0.7188 | 0.8646 | | 0.3508 | 3.06 | 5400 | 0.7017 | 0.8656 | | 0.295 | 3.12 | 5500 | 0.6982 | 0.8653 | | 0.295 | 3.18 | 5600 | 0.7031 | 0.8655 | | 0.295 | 3.23 | 5700 | 0.6992 | 0.8651 | | 0.295 | 3.29 | 5800 | 0.6997 | 0.8653 | | 0.295 | 3.35 | 5900 | 0.7041 | 0.8651 | | 0.2348 | 3.41 | 6000 | 0.7075 | 0.8649 | | 0.2348 | 3.46 | 6100 | 0.6992 | 0.8650 | | 0.2348 | 3.52 | 6200 | 0.7065 | 0.8647 | | 0.2348 | 3.58 | 6300 | 0.6997 | 0.8652 | | 0.2348 | 3.63 | 6400 | 0.7026 | 0.8651 | | 0.2411 | 3.69 | 6500 | 0.7046 | 0.8656 | | 0.2411 | 3.75 | 6600 | 0.7007 | 0.8655 | | 0.2411 | 3.8 | 6700 | 0.7026 | 0.8651 | | 0.2411 | 3.86 | 6800 | 0.7031 | 0.8655 | | 0.2411 | 3.92 | 6900 | 0.7012 | 0.8658 | | 0.251 | 3.97 | 7000 | 0.7051 | 0.8656 | | 0.251 | 4.03 | 7100 | 0.7607 | 0.8650 | | 0.251 | 4.09 | 7200 | 0.7632 | 0.8656 | | 0.251 | 4.14 | 7300 | 0.7588 | 0.8655 | | 0.251 | 4.2 | 7400 | 0.7578 | 0.8651 | | 0.1797 | 4.26 | 7500 | 0.7710 | 0.8645 | | 0.1797 | 4.31 | 7600 | 0.7627 | 0.8648 | | 0.1797 | 4.37 | 7700 | 0.7583 | 0.8650 | | 0.1797 | 4.43 | 7800 | 0.7646 | 0.8649 | | 0.1797 | 4.48 | 7900 | 0.7598 | 0.8646 | | 0.1784 | 4.54 | 8000 | 0.7656 | 0.8650 | | 0.1784 | 4.6 | 8100 | 0.7617 | 0.8648 | | 0.1784 | 4.65 | 8200 | 0.7573 | 0.8651 | | 0.1784 | 4.71 | 8300 | 0.7671 | 0.8648 | | 0.1784 | 4.77 | 8400 | 0.7563 | 0.8651 | | 0.1827 | 4.82 | 8500 | 0.7651 | 0.8649 | | 0.1827 | 4.88 | 8600 | 0.7637 | 0.8650 | | 0.1827 | 4.94 | 8700 | 0.7607 | 0.8654 | | 0.1827 | 4.99 | 8800 | 0.7607 | 0.8650 | | 0.1827 | 5.05 | 8900 | 0.8149 | 0.8646 | | 0.167 | 5.11 | 9000 | 0.8081 | 0.8648 | | 0.167 | 5.16 | 9100 | 0.8184 | 0.8644 | | 0.167 | 5.22 | 9200 | 0.8140 | 0.8647 | | 0.167 | 5.28 | 9300 | 0.8169 | 0.8644 | | 0.167 | 5.33 | 9400 | 0.8120 | 0.8645 | | 0.1371 | 5.39 | 9500 | 0.8154 | 0.8643 | | 0.1371 | 5.45 | 9600 | 0.8179 | 0.8642 | | 0.1371 | 5.51 | 9700 | 0.8154 | 0.8643 | | 0.1371 | 5.56 | 9800 | 0.8120 | 0.8645 | | 0.1371 | 5.62 | 9900 | 0.8110 | 0.8650 | | 0.1425 | 5.68 | 10000 | 0.8159 | 0.8645 | | 0.1425 | 5.73 | 10100 | 0.8174 | 0.8646 | | 0.1425 | 5.79 | 10200 | 0.8159 | 0.8649 | | 0.1425 | 5.85 | 10300 | 0.8110 | 0.8639 | | 0.1425 | 5.9 | 10400 | 0.8135 | 0.8645 | | 0.1505 | 5.96 | 10500 | 0.8140 | 0.8642 | | 0.1505 | 6.02 | 10600 | 0.8628 | 0.8640 | | 0.1505 | 6.07 | 10700 | 0.8540 | 0.8644 | | 0.1505 | 6.13 | 10800 | 0.8530 | 0.8642 | | 0.1505 | 6.19 | 10900 | 0.8560 | 0.8647 | | 0.1086 | 6.24 | 11000 | 0.8555 | 0.8649 | | 0.1086 | 6.3 | 11100 | 0.8604 | 0.8644 | | 0.1086 | 6.36 | 11200 | 0.8569 | 0.8642 | | 0.1086 | 6.41 | 11300 | 0.8530 | 0.8639 | | 0.1086 | 6.47 | 11400 | 0.8589 | 0.8643 | | 0.1076 | 6.53 | 11500 | 0.8525 | 0.8639 | | 0.1076 | 6.58 | 11600 | 0.8579 | 0.8640 | | 0.1076 | 6.64 | 11700 | 0.8594 | 0.8640 | | 0.1076 | 6.7 | 11800 | 0.8599 | 0.8643 | | 0.1076 | 6.75 | 11900 | 0.8564 | 0.8640 | | 0.1109 | 6.81 | 12000 | 0.8633 | 0.8640 | | 0.1109 | 6.87 | 12100 | 0.8584 | 0.8638 | | 0.1109 | 6.92 | 12200 | 0.8647 | 0.8636 | | 0.1109 | 6.98 | 12300 | 0.8599 | 0.8635 | | 0.1109 | 7.04 | 12400 | 0.8979 | 0.8632 | | 0.1028 | 7.09 | 12500 | 0.8936 | 0.8635 | | 0.1028 | 7.15 | 12600 | 0.9043 | 0.8637 | | 0.1028 | 7.21 | 12700 | 0.8989 | 0.8642 | | 0.1028 | 7.26 | 12800 | 0.8936 | 0.8642 | | 0.1028 | 7.32 | 12900 | 0.8921 | 0.8641 | | 0.0774 | 7.38 | 13000 | 0.8955 | 0.8634 | | 0.0774 | 7.43 | 13100 | 0.8950 | 0.8636 | | 0.0774 | 7.49 | 13200 | 0.8994 | 0.8635 | | 0.0774 | 7.55 | 13300 | 0.8999 | 0.8635 | | 0.0774 | 7.6 | 13400 | 0.8936 | 0.8631 | | 0.0852 | 7.66 | 13500 | 0.9048 | 0.8634 | | 0.0852 | 7.72 | 13600 | 0.8960 | 0.8632 | | 0.0852 | 7.78 | 13700 | 0.9023 | 0.8635 | | 0.0852 | 7.83 | 13800 | 0.8984 | 0.8638 | | 0.0852 | 7.89 | 13900 | 0.9019 | 0.8635 | | 0.0879 | 7.95 | 14000 | 0.9014 | 0.8634 | | 0.0879 | 8.0 | 14100 | 0.9136 | 0.8630 | | 0.0879 | 8.06 | 14200 | 0.9312 | 0.8639 | | 0.0879 | 8.12 | 14300 | 0.9346 | 0.8635 | | 0.0879 | 8.17 | 14400 | 0.9307 | 0.8635 | | 0.0611 | 8.23 | 14500 | 0.9419 | 0.8641 | | 0.0611 | 8.29 | 14600 | 0.9331 | 0.8631 | | 0.0611 | 8.34 | 14700 | 0.9375 | 0.8636 | | 0.0611 | 8.4 | 14800 | 0.9292 | 0.8626 | | 0.0611 | 8.46 | 14900 | 0.9458 | 0.8637 | | 0.061 | 8.51 | 15000 | 0.9336 | 0.8634 | | 0.061 | 8.57 | 15100 | 0.9409 | 0.8630 | | 0.061 | 8.63 | 15200 | 0.9390 | 0.8632 | | 0.061 | 8.68 | 15300 | 0.9375 | 0.8628 | | 0.061 | 8.74 | 15400 | 0.9365 | 0.8630 | | 0.0646 | 8.8 | 15500 | 0.9370 | 0.8628 | | 0.0646 | 8.85 | 15600 | 0.9355 | 0.8629 | | 0.0646 | 8.91 | 15700 | 0.9375 | 0.8632 | | 0.0646 | 8.97 | 15800 | 0.9390 | 0.8630 | | 0.0646 | 9.02 | 15900 | 0.9717 | 0.8630 | | 0.0593 | 9.08 | 16000 | 0.9673 | 0.8626 | | 0.0593 | 9.14 | 16100 | 0.9644 | 0.8630 | | 0.0593 | 9.19 | 16200 | 0.9624 | 0.8631 | | 0.0593 | 9.25 | 16300 | 0.9648 | 0.8633 | | 0.0593 | 9.31 | 16400 | 0.9673 | 0.8632 | | 0.0415 | 9.36 | 16500 | 0.9658 | 0.8633 | | 0.0415 | 9.42 | 16600 | 0.9688 | 0.8628 | | 0.0415 | 9.48 | 16700 | 0.9653 | 0.8632 | | 0.0415 | 9.53 | 16800 | 0.9658 | 0.8628 | | 0.0415 | 9.59 | 16900 | 0.9668 | 0.8629 | | 0.0471 | 9.65 | 17000 | 0.9604 | 0.8625 | | 0.0471 | 9.7 | 17100 | 0.9658 | 0.8621 | | 0.0471 | 9.76 | 17200 | 0.9731 | 0.8630 | | 0.0471 | 9.82 | 17300 | 0.9692 | 0.8626 | | 0.0471 | 9.88 | 17400 | 0.9673 | 0.8623 | | 0.0528 | 9.93 | 17500 | 0.9614 | 0.8620 | | 0.0528 | 9.99 | 17600 | 0.9697 | 0.8621 | | 910cba5aa10f8ada63d0b06cb6d39e39 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'dreambooth', 'anime'] | false | A fined-tuned stable diffusion model for generating Padorus. **Token:** `PadoruMeme` (use this in your prompt to utilise the style)<br> **Class Phrase:** `1girl` (also use this in the prompt) [Model Download](https://huggingface.co/joujiboi/Padoru-Diffusion/resolve/main/2022-12-12T19-38-27_Padoru_1_training_images_2500_max_training_steps_PadoruMeme_token_1girl_class_word.ckpt) Examples:     | da8fc8c48289aed68a92a2721c8e4097 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qqp-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qqp](https://huggingface.co/muhtasham/tiny-mlm-glue-qqp) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7213 - Matthews Correlation: 0.0938 | 4d2eaf01b5e5fefc1b40d44fe67653a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6096 | 1.87 | 500 | 0.6213 | 0.0 | | 0.6002 | 3.73 | 1000 | 0.6164 | 0.0 | | 0.5831 | 5.6 | 1500 | 0.6190 | 0.0583 | | 0.5559 | 7.46 | 2000 | 0.6402 | 0.0849 | | 0.528 | 9.33 | 2500 | 0.6572 | 0.1149 | | 0.5109 | 11.19 | 3000 | 0.6663 | 0.1134 | | 0.4867 | 13.06 | 3500 | 0.6832 | 0.1024 | | 0.4677 | 14.93 | 4000 | 0.7213 | 0.0938 | | 77e68fd36b6161b1623c9f347c068f6d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Tamil 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 ta dataset. It achieves the following results on the evaluation set: - Loss: 0.1727 - Wer: 8.4538 | ec6e2f93d0390a34bdf10e320a601aa4 |
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: 32 - eval_batch_size: 16 - 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: 50 - training_steps: 1000 | a02be9e137e01cb6df39f620609effda |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0723 | 1.27 | 1000 | 0.1727 | 8.4538 | | 5342afdad9b7d43518bca344c9706842 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | roberta-base-japanese-jsnli This model is a fine-tuned version of [nlp-waseda/roberta-base-japanese](https://huggingface.co/nlp-waseda/roberta-base-japanese) on the [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset. It achieves the following results on the evaluation set: - Loss: 0.2039 - Accuracy: 0.9328 | 2dd93328d52db43455d13c1c6973b521 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Simple zero-shot classification pipeline ```python from transformers import pipeline from pyknp import Juman juman = Juman() classifier = pipeline("zero-shot-classification", model="Formzu/roberta-base-japanese-jsnli") sequence_to_classify = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()]) candidate_labels = ['旅行', '料理', '踊り'] out = classifier(sequence_to_classify, candidate_labels, hypothesis_template="この 例 は {} です 。") print(out) | 223ec72a2bdc621a8753ebb7606becc0 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch from pyknp import Juman juman = Juman() device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "Formzu/roberta-base-japanese-jsnli" model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device) tokenizer = AutoTokenizer.from_pretrained(model_name) premise = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()]) label = '旅行' hypothesis = f'この 例 は {label} です 。' input = tokenizer.encode(premise, hypothesis, return_tensors='pt').to(device) with torch.no_grad(): logits = model(input)["logits"][0] probs = logits.softmax(dim=-1) print(probs.cpu().numpy(), logits.cpu().numpy()) | 9699296cc8b970fed8d09a25968f4db5 |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | b59fbbce42e464557e0bd859b436eb3f |
cc-by-sa-4.0 | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4067 | 1.0 | 16657 | 0.2224 | 0.9201 | | 0.3397 | 2.0 | 33314 | 0.2152 | 0.9208 | | 0.2775 | 3.0 | 49971 | 0.2039 | 0.9328 | | b8f5663ba4dd702ab1c01eba3ea8270d |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0225 - Validation Loss: 0.0519 - Epoch: 2 | afe9de77b9b2c11fe4b023e3f0aa53c1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0226 | 0.0519 | 0 | | 0.0229 | 0.0519 | 1 | | 0.0225 | 0.0519 | 2 | | 2d75b20e0fc60077ed8d9731739ce061 |
mit | ['PyTorch', 'Transformers', 'text generation'] | false | RuGPT2_Gen_Comments Предварительно обученная модель на русском языке с использованием языковой модели "sberbank-ai/rugpt3small_based_on_gpt2". Содержимое карты этой модели было создано, чтобы дополнить предоставленную информацию и привести конкретные примеры её использования. | 3413c2b7fc954f5c620cbf73323fa887 |
mit | ['PyTorch', 'Transformers', 'text generation'] | false | Описание модели RuGPT2_Gen_Comments — это модель предназначена для демонстрации генерации новостей, предварительно обученная на массиве данных Lenta2 проекта CORUS на русском языке. Входные данные — это последовательности непрерывного текста определенной длины (block_size = 1048). | 9586a712f173793503f0f034b6ad79bd |
mit | ['PyTorch', 'Transformers', 'text generation'] | false | Проимер использования ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dmitriy007/rugpt2_gen_comments") model = AutoModelForCausalLM.from_pretrained("Dmitriy007/rugpt2_gen_comments") input_text = 'Ученик старшего класса лицея № 21 Иван Сидоров из города Адлер полетел в космос на планету Марс.' inputs = tokenizer(input_text, return_tensors="pt") model.to('cuda') inputs.to('cuda') input_ids = inputs["input_ids"] output = model.generate( input_ids, attention_mask=inputs["attention_mask"], pad_token_id=model.config.bos_token_id, max_length=300, num_beams=5, num_return_sequences=1, top_k=50, top_p=0.90, no_repeat_ngram_size=2, temperature=0.7, early_stopping=True ) generated_text = list(map(tokenizer.decode, output)) print(generated_text[0]) ``` | 1aa90b8c238fbf997f98aef1b64c48b7 |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-squad2-finetuned-islamic-squad This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4152 | 64cfec58e29599daa4d7586fd3b9f4d6 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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 - num_epochs: 3.0 | 23974d0ef69d8f147af09c852bdf5fcb |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.3 | 100 | 0.3653 | | No log | 2.6 | 200 | 0.4152 | | 2126a8876e82dba73df1f05ea3a91007 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Model Details **Model Description:** KLUE BERT base is a pre-trained BERT Model on Korean Language. The developers of KLUE BERT base developed the model in the context of the development of the [Korean Language Understanding Evaluation (KLUE) Benchmark](https://arxiv.org/pdf/2105.09680.pdf). - **Developed by:** See [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) for model developers - **Model Type:** Transformer-based language model - **Language(s):** Korean - **License:** cc-by-sa-4.0 - **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model. - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/2105.09680) - [GitHub Repo](https://github.com/KLUE-benchmark/KLUE) | 0eb063f9644b9130e76c132cd17d544d |
cc-by-sa-4.0 | ['korean', 'klue'] | false | How to Get Started With the Model ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("klue/bert-base") tokenizer = AutoTokenizer.from_pretrained("klue/bert-base") ``` | 91c35d9d442e2acc2bae779d609def2e |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Direct Use The model can be used for tasks including topic classification, semantic textual similarity, natural language inference, named entity recognition, and other tasks outlined in the [KLUE Benchmark](https://github.com/KLUE-benchmark/KLUE). | 08e98d19d27441d00a187e1349af7bfb |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Risks, Limitations and Biases Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The model developers discuss several ethical considerations related to the model in the [paper](https://arxiv.org/pdf/2105.09680.pdf), including: - Bias issues with the publicly available data used in the pretraining corpora (and considerations related to filtering) - PII in the data used in the pretraining corpora (and efforts to pseudonymize the data) For ethical considerations related to the KLUE Benchmark, also see the [paper](https://arxiv.org/pdf/2105.09680.pdf). | f9191f80cbc53e8df41a57c649aad6c8 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Training Data The authors use the following pretraining corpora for the model, described in the [associated paper](https://arxiv.org/pdf/2105.09680.pdf): > We gather the following five publicly available Korean corpora from diverse sources to cover a broad set of topics and many different styles. We combine these corpora to build the final pretraining corpus of size approximately 62GB. > > - **MODU:** [Modu Corpus](https://corpus.korean.go.kr) is a collection of Korean corpora distributed by [National Institute of Korean Languages](https://corpus.korean.go.kr/). It includes both formal articles (news and books) and colloquial text (dialogues). > - **CC-100-Kor:** [CC-100](https://data.statmt.org/cc-100/) is the large-scale multilingual web crawled corpora by using CC-Net ([Wenzek et al., 2020](https://www.aclweb.org/anthology/2020.lrec-1.494)). This is used for training XLM-R ([Conneau et al., 2020](https://aclanthology.org/2020.acl-main.747/)). We use the Korean portion from this corpora. > - **NAMUWIKI:** NAMUWIKI is a Korean web-based encyclopedia, similar to Wikipedia, but known to be less formal. Specifically, we download [the dump](http://dump.thewiki.kr) created on March 2nd, 2020. > - **NEWSCRAWL:** NEWSCRAWL consists of 12,800,000 news articles published from 2011 to 2020, collected from a news aggregation platform. > - **PETITION:** Petition is a collection of public petitions posted to the Blue House asking for administrative actions on social issues. We use the articles in the [Blue House National Petition](https://www1.president.go.kr/petitions) published from [August 2017 to March 2019](https://ko-nlp.github.io/Korpora/en-docs/corpuslist/korean_petitions.html). The authors also describe ethical considerations related to the pretraining corpora in the [associated paper](https://arxiv.org/pdf/2105.09680.pdf). | 5ae34826a453193b4a18da729bf49009 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Preprocessing The authors describe their preprocessing procedure in the [associated paper](https://arxiv.org/pdf/2105.09680.pdf): > We filter noisy text and non-Korean text using the same methods from Section 2.3 (of the paper). Each document in the corpus is split into sentences using C++ implementation (v1.3.1.) of rule-based [Korean Sentence Splitter (KSS)](https://github.com/likejazz/korean-sentence-splitter). For CC-100-Kor and NEWSCRAWL, we keep sentences of length greater than equal to 200 characters, as a heuristics to keep well-formed sentences. We then remove sentences included in our benchmark task datasets, using BM25 as a sentence similarity metric ([reference](https://www.microsoft.com/en-us/research/publication/okapi-at-trec-3/)). | 57a0c55d41567746fbb2fd5c69bd1001 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Tokenization The authors describe their tokenization procedure in the [associated paper](https://arxiv.org/pdf/2105.09680.pdf): > We design and use a new tokenization method, morpheme-based subword tokenization. When building a vocabulary, we pre-tokenize a raw text into morphemes using a morphological analyzer, and then we apply byte pair encoding (BPE) ([Senrich et al., 2016](https://aclanthology.org/P16-1162/)) to get the final vocabulary. For morpheme segmentation, we use [Mecab-ko](https://bitbucket.org/eunjeon/mecab-ko), MeCab ([Kudo, 2006](https://taku910.github.io/mecab/)) adapted for Korean, and for BPE segmentation, we use the wordpiece tokenizer from [Huggingface Tokenizers library](https://github.com/huggingface/tokenizers). We specify the vocabulary size to 32k. After building the vocabulary, we only use the BPE model during inference, which allows us to tokenize a word sequence by reflecting morphemes without a morphological analyzer. This improves both usability and speed. The training configurations are further described in the [paper](https://arxiv.org/pdf/2105.09680.pdf). | d5366a01f6ad69cad4a638fe66c2db41 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Testing Data, Factors and Metrics The model was evaluated on the [KLUE Benchmark](https://github.com/KLUE-benchmark/KLUE). The tasks and metrics from the KLUE Benchmark that were used to evaluate this model are described briefly below. For more information about the KLUE Benchmark, see the [data card](https://huggingface.co/datasets/klue), [Github Repository](https://github.com/KLUE-benchmark/KLUE), and [associated paper](https://arxiv.org/pdf/2105.09680.pdf). - **Task:** Topic Classification (TC) - Yonhap News Agency Topic Classification (YNAT), **Metrics:** Macro F1 score, defined as the mean of topic-wise F1 scores, giving the same importance to each topic. - **Task:** Semantic Textual Similarity (STS), **Metrics:** Pearsons' correlation coefficient (Pearson’ r) and F1 score - **Task:** Natural Language Inference (NLI), **Metrics:** Accuracy - **Task:** Named Entity Recognition (NER), **Metrics:** Entity-level macro F1 (Entity F1) and character-level macro F1 (Char F1) scores - **Task:** Relation Extraction (RE), **Metrics:** Micro F1 score on relation existing cases and area under the precision- recall curve (AUPRC) on all classes - **Task:** Dependency Parsing (DP), **Metrics:** Unlabeled attachment score (UAS) and labeled attachment score (LAS) - **Task:** Machine Reading Comprehension (MRC), **Metrics:** Exact match (EM) and character-level ROUGE-W (ROUGE), which can be viewed as longest common consecutive subsequence (LCCS)-based F1 score. - **Task:** Dialogue State Tracking (DST), **Metrics:** Joint goal accuracy (JGA) and slot micro F1 score (Slot F1) | ed424551a9376282da7f6bfffacb35eb |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Results | Task | TC | STS | | NLI | NER | | RE | | DP | | MRC | | DST | | | :---: |:---: | :---: | :---: |:---:| :---: | :---: |:---:| :---:| :---: |:---: | :---: | :---:| :---: | :---: | | Metric | F1 | Pearsons' r| F1 | ACC | Entity F1 | Char F1 | F1 | AUPRC| UAS | LAS | EM | ROUGE| JGA |Slot F1 | | | 85.73| 90.85 | 82.84 |81.63| 83.97 | 91.39 |66.44| 66.17| 89.96 |88.05 | 62.32 | 68.51| 46.64 | 91.61 | | 033f40d593ed76bdb81ec69bc746618b |
cc-by-sa-4.0 | ['korean', 'klue'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf). - **Hardware Type:** TPU v3-8 - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown | ed4b41396ebad481abcfbd141491d813 |
cc-by-sa-4.0 | ['korean', 'klue'] | false | Citation Information ```bibtex @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 1988d70d99a2407dfd39f6f9eda00095 |
cc-by-4.0 | [] | false | Readability benchmark (ES): mbert-en-es-sentences-3class This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish". You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark). | 347bae272f590c397008ce65e9666176 |
cc-by-4.0 | [] | false | Models Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets. You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link). These are the available models you can use (current model page in bold): | Model | Granularity | | 6aa5cdba399d534994680f6264885da6 |
cc-by-4.0 | [] | false | classes | |-----------------------------------------------------------------------------------------------------------|----------------|:---------:| | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class) | paragraphs | 2 | | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-3class) | paragraphs | 3 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-2class) | paragraphs | 2 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-3class) | paragraphs | 3 | | [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-paragraphs-3class) | paragraphs | 3 | | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-2class) | sentences | 2 | | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-3class) | sentences | 3 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-2class) | sentences | 2 | | [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class) | sentences | 3 | | **[mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-sentences-3class)** | **sentences** | **3** | For the zero-shot setting, we used the original models [BERTIN](bertin-project/bertin-roberta-base-spanish) and [mBERT](https://huggingface.co/bert-base-multilingual-uncased) with no further training. | ec7288b09c495908c070d1858db5609c |
apache-2.0 | ['translation'] | false | opus-mt-fr-bcl * source languages: fr * target languages: bcl * OPUS readme: [fr-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bcl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bcl/opus-2020-01-09.eval.txt) | e237fe1e3588c4ae714f63ad10027d21 |
mit | ['generated_from_trainer'] | false | esm2_t6_8M_UR50D-pfam-test-wed This model is a fine-tuned version of [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8768 - Accuracy: 0.8360 | 4ecdd1ed9b1e1ce7b68300f1333ed0e1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | e47efb5da8289c6d567be741be580e6c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.0443 | 0.44 | 15000 | 2.7588 | 0.4561 | | 1.7448 | 0.88 | 30000 | 1.5400 | 0.6833 | | 1.2082 | 1.33 | 45000 | 1.0888 | 0.7837 | | 1.0505 | 1.77 | 60000 | 0.8768 | 0.8360 | | 78fb5c8fdf45bb70425781f002a6509b |
apache-2.0 | ['generated_from_trainer'] | false | airlinesentiment This model is a fine-tuned version of [PDatt/outcome](https://huggingface.co/PDatt/outcome) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2552 - Accuracy: 0.9587 - F1: 0.9586 - Precision: 0.9585 - Recall: 0.9587 | 08b1ef37414d99752146000a5502776e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech_s809 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 2bd59f486aea7c4fbdbaae7c5a38b92b |
apache-2.0 | ['translation'] | false | opus-mt-en-swc * source languages: en * target languages: swc * OPUS readme: [en-swc](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-swc/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-swc/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-swc/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-swc/opus-2020-01-08.eval.txt) | 60576340c84bca09894fcc27ae824a66 |
apache-2.0 | ['translation'] | false | opus-mt-en-af * source languages: en * target languages: af * OPUS readme: [en-af](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-af/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-af/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-af/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-af/opus-2019-12-18.eval.txt) | 8feda13cc50c291795783b47f025a5ff |
unknown | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Asmongold model.ckpt for Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. I've trained using Dreambooth 20 images of twitch streamer Asmongold for the purpose of text-to-image illustration generation using Stable Diffusion. Feel free to download, use and share the model as you like. To give the Ai the trigger to generate an illustration based on the trained Asmongold images, make sure to use the tag "asmonbald" in your prompts. Example: a detailed portrait photo of a man vs a detailed portrait photo of asmonbald --- | 624b073eeff5b3b2b1910e1ba405d8ad |
apache-2.0 | ['mobile', 'vison', 'image-classification'] | false | Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> EfficientFormer-L1, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. This checkpoint of EfficientFormer-L1 was trained for 300 epochs. - Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren - Language(s): English - License: This model is licensed under the apache-2.0 license - Resources for more information: - [Research Paper](https://arxiv.org/abs/2206.01191) - [GitHub Repo](https://github.com/snap-research/EfficientFormer/) </model_details> <how_to_start> | 52767897bb94e64b069d817601465be0 |
mit | [] | false | Stable Diffusion Artist Collaboration → Model 2 This is the `<model-2>` concept taught to stable diffusion via textual inversion training. Anyone is free to load this concept into the [stable conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). More information on this collaboration and selected output images can be found here → https://www.astronaut.horse/model-2 Below are the original artworks used as input images. <img style="width: 100%; max-width: 500px;" src="https://huggingface.co/sd-concepts-library/enk-resin-frames/resolve/main/concept_images/2.jpeg"> <img style="width: 100%; max-width: 500px;" src="https://huggingface.co/sd-concepts-library/enk-resin-frames/resolve/main/concept_images/4.jpeg"> <img style="width: 100%; max-width: 500px;" src="https://huggingface.co/sd-concepts-library/enk-resin-frames/resolve/main/concept_images/0.jpeg"> <img style="width: 100%; max-width: 500px;" src="https://huggingface.co/sd-concepts-library/enk-resin-frames/resolve/main/concept_images/1.jpeg"> <img style="width: 100%; max-width: 500px;" src="https://huggingface.co/sd-concepts-library/enk-resin-frames/resolve/main/concept_images/3.jpeg"> | 52efdafd642704c8a89bfbebe5fbe973 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | ba79ed0547b1c3e0e5cc6ec2e4c1dbc1 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-upos") s="国境の長いトンネルを抜けると雪国であった。" t=tokenizer.tokenize(s) p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(t,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/deberta-base-japanese-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` | 7e5d1e550bd8bbf150e12f17ff4a040e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Accuracy: 0.5271 | 1dad33ca34cc3efee2f9ebd9ef3e1a84 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6952 | 1.0 | 10 | 0.6929 | 0.5271 | | 0.6935 | 2.0 | 20 | 0.6924 | 0.5271 | | 0.6944 | 3.0 | 30 | 0.6930 | 0.5271 | | 0.6944 | 4.0 | 40 | 0.6930 | 0.5271 | | 0.6944 | 5.0 | 50 | 0.6944 | 0.4729 | | 0.6931 | 6.0 | 60 | 0.6921 | 0.5271 | | 0.6942 | 7.0 | 70 | 0.6926 | 0.5271 | | 0.6937 | 8.0 | 80 | 0.6939 | 0.4729 | | 0.6934 | 9.0 | 90 | 0.6921 | 0.5271 | | 0.694 | 10.0 | 100 | 0.6920 | 0.5271 | | 0.6937 | 11.0 | 110 | 0.6945 | 0.4729 | | 0.6934 | 12.0 | 120 | 0.6928 | 0.5271 | | 0.6934 | 13.0 | 130 | 0.6924 | 0.5271 | | 0.6934 | 14.0 | 140 | 0.6935 | 0.4729 | | 0.6937 | 15.0 | 150 | 0.6944 | 0.4729 | | 7692bba3f0c3bf2121e5cb07ae5e7d97 |
apache-2.0 | [] | false | Bert Base model HPU configuration This model only contains the `GaudiConfig` file for running the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP) - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html | 0c4686a2b185d0bdbd656546f27a1c4d |
apache-2.0 | [] | false | Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with BERT with the following command: ```bash python run_qa.py \ --model_name_or_path bert-base-uncased \ --gaudi_config_name Habana/bert-base-uncased \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 24 \ --per_device_eval_batch_size 8 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --output_dir /tmp/squad/ \ --use_habana \ --use_lazy_mode \ --throughput_warmup_steps 2 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. | 254639d28fd668a3d74b726a4b63e20c |
mit | ['generated_from_keras_callback'] | false | Deep98/Materialism-clustered This model is a fine-tuned version of [nandysoham16/7-clustered_aug](https://huggingface.co/nandysoham16/7-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0705 - Train End Logits Accuracy: 0.9896 - Train Start Logits Accuracy: 0.9722 - Validation Loss: 0.2530 - Validation End Logits Accuracy: 0.5 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | f68d0d888cac585f5dc20d314b54ba51 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.0705 | 0.9896 | 0.9722 | 0.2530 | 0.5 | 1.0 | 0 | | 8b8305546ca89e44694597a258db5830 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | luoshaliya1 Dreambooth model trained by jiaheillu Sample pictures of this concept:       | e7912d112d2d50b2b549c67c118d71d3 |
mit | ['generated_from_trainer'] | false | output This model is a fine-tuned version of [rinna/japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1545 - Accuracy: 0.4936 | 666e537d65b9be21ae2f7c54dae9aade |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | c2c49a1b13996ad190169b233b1cbb65 |
apache-2.0 | ['translation'] | false | spa-tgl * source group: Spanish * target group: Tagalog * OPUS readme: [spa-tgl](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-tgl/README.md) * model: transformer-align * source language(s): spa * target language(s): tgl_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-tgl/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-tgl/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-tgl/opus-2020-06-17.eval.txt) | 4c51c473a600a44af6f63d850364bab2 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-tgl - source_languages: spa - target_languages: tgl - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-tgl/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'tl'] - src_constituents: {'spa'} - tgt_constituents: {'tgl_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-tgl/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-tgl/opus-2020-06-17.test.txt - src_alpha3: spa - tgt_alpha3: tgl - short_pair: es-tl - chrF2_score: 0.5379999999999999 - bleu: 24.7 - brevity_penalty: 1.0 - ref_len: 4422.0 - src_name: Spanish - tgt_name: Tagalog - train_date: 2020-06-17 - src_alpha2: es - tgt_alpha2: tl - prefer_old: False - long_pair: spa-tgl - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | c51f34b3fd7c99796d321d4c251eab00 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-Czech Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | 1164acad512d7af079974bae43fd98cb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "cs", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Czech") model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Czech") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 08e79b85299faeb88f1d82a7e5ef8892 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Czech test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "cs", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Czech") model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Czech") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | cc37707955f6518dd572227d9e94b762 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 27.047806 % | 9ee7878ec61cfdfd0f252b518ff56e54 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_mrpc This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6145 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | 378624d1793bc82197be187704ae98d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6377 | 1.0 | 29 | 0.6240 | 0.6838 | 0.8122 | 0.7480 | | 0.6309 | 2.0 | 58 | 0.6236 | 0.6838 | 0.8122 | 0.7480 | | 0.6306 | 3.0 | 87 | 0.6233 | 0.6838 | 0.8122 | 0.7480 | | 0.6291 | 4.0 | 116 | 0.6226 | 0.6838 | 0.8122 | 0.7480 | | 0.6222 | 5.0 | 145 | 0.6145 | 0.6838 | 0.8122 | 0.7480 | | 0.5736 | 6.0 | 174 | 0.6208 | 0.7010 | 0.7939 | 0.7474 | | 0.488 | 7.0 | 203 | 0.6414 | 0.6936 | 0.7795 | 0.7366 | | 0.3939 | 8.0 | 232 | 0.7659 | 0.7279 | 0.8122 | 0.7701 | | 0.3038 | 9.0 | 261 | 0.8875 | 0.7083 | 0.8027 | 0.7555 | | 0.2636 | 10.0 | 290 | 0.9829 | 0.7034 | 0.8033 | 0.7533 | | 87eac51e69e2cba593dabae4e6c4d420 |
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