license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | tgf-xlm-roberta-base-pt-br This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [BrWac](https://huggingface.co/datasets/thegoodfellas/brwac_tiny) dataset. | 3306a19ae8efaaa310ce73d73dd1a597 |
mit | [] | false | Model description This is a fine-tuned version of the Brazilian Portuguese language. It was trained using the [BrWac](https://huggingface.co/datasets/thegoodfellas/brwac_tiny) dataset and followed the principles from [Roberta's paper](https://arxiv.org/abs/1907.11692). The key strategies are: 1. *Full-Sentences*: Quoted from the paper: "Each input is packed with full sentences sampled contiguously from one or more documents, such that the total length is at most 512 tokens. Inputs may cross document boundaries. When we reach the end of one document, we begin sampling sentences from the next document and add an extra separator token between documents". 2. Tunned hyperparameters: adam_beta1=0.9, adam_beta2=0.98, adam_epsilon=1e-6 (as paper suggests) | 2bce364c48d3d29216a80ef13523e3e2 |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-4 - train_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP | 05984bbf34219e8bfe1c73130b971043 |
mit | [] | false | Environment 4xA100.88V NVIDIA Special thanks to [DataCrunch.io](https://datacrunch.io) with their amazing, and affordable GPUs. <img src="https://datacrunch.io/_next/static/media/Logo.6b773500.svg" width="20%"/> | 9cfcb5912405caec142c02e76318a748 |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-v4-e1-feedback-e4 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9640 - Rouge1: 61.6305 - Rouge2: 41.9892 - Rougel: 57.0694 - Rougelsum: 58.3816 - Gen Len: 19.0 | 65e6d07ab90b0407714d2e001209c56d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 4a549011f600b28083cf49dc75acf365 |
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 | 34 | 2.8512 | 67.5001 | 46.2823 | 62.2247 | 63.3811 | 18.875 | | No log | 2.0 | 68 | 2.3116 | 62.1089 | 43.432 | 57.564 | 58.8003 | 19.0 | | No log | 3.0 | 102 | 2.0519 | 61.2025 | 40.9901 | 56.3369 | 57.5829 | 19.0 | | No log | 4.0 | 136 | 1.9640 | 61.6305 | 41.9892 | 57.0694 | 58.3816 | 19.0 | | 67bc4b9dac6fc0fe74aa9faa5b237d8f |
mit | [] | false | valorantstyle on Stable Diffusion This is the `<valorant>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train 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). Here is the new concept you will be able to use as a `style`:      | bb29124a720d114ec99ef1a6f647afd6 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset. Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable. On mozilla-foundation/common_voice_8_0 it achieved: - cer: 23.64% On speech-recognition-community-v2/dev_data it achieved: - cer: 30.99% It achieves the following results on the evaluation set: - Loss: 0.5212 - Wer: 1.3068 | 69f80fe4ca2c1a84d1b994e621e49c2f |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 48 - 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP | 529a6c38f5a5118dd04e28705798f848 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 | | 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 | | 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 | | 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 | | 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 | | 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 | | 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 | | 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 | | 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 | | 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 | | 69f75f8a957e10c74d250ef3cf4a6a1f |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs ``` 2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` | 8fce1bd056aa933636ca894351643398 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Turkish This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on the [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 8319ad008b6e1459dd55c598956569e5 |
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", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish") model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish") | 46ff2e9269a5ad1ca6a333d3dd654a59 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` | 5da678be9ba6a1257ae196ecc68e2691 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish 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", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish") model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' | 69cc22f2b691402fdcaea916fdf7e432 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | e4e2df7013bd3e3ab04a323f4557b1ba |
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**: 21.13 % | e5dc041edad4c5663bea0395610688f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8085 | 1.0 | 250 | 0.3033 | 0.9065 | 0.9037 | | 0.2458 | 2.0 | 500 | 0.2133 | 0.9265 | 0.9265 | | 5dc5d82c840b77ba48e5738c178609ec |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-ncj/nah Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Nahuatl specifically of the Nort of Puebla (ncj) using a derivate of [SLR92](https://www.openslr.org/92/), and some samples of `es` and `de` datasets from [Common Voice](https://huggingface.co/datasets/common_voice). | 21a7f62f372997ef4abb3183d0800a3a |
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", "{lang_id}", split="test[:2%]") | acea915a9fa2311cc8b1723f86179d17 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: publish nahuatl_slr92_by_sentence processor = Wav2Vec2Processor.from_pretrained("tyoc213/wav2vec2-large-xlsr-nahuatl") model = Wav2Vec2ForCTC.from_pretrained("tyoc213/wav2vec2-large-xlsr-nahuatl") resampler = torchaudio.transforms.Resample(48_000, 16_000) | d7e275015ac93c653489fed23d064200 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Nahuatl specifically of the Nort of Puebla (ncj) 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", "{lang_id}", split="test") | bdc05068e7b05e0a4ed0f16554418586 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: publish nahuatl_slr92_by_sentence wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("tyoc213/wav2vec2-large-xlsr-nahuatl") model = Wav2Vec2ForCTC.from_pretrained("tyoc213/wav2vec2-large-xlsr-nahuatl") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\"\“\%\‘\”\�\(\)\-]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 5b3b6fc82e1029ad6645750fd82f6750 |
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**: 50.95 % | bfff8161b6354d3cc09e02687261712d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training A derivate of [SLR92](https://www.openslr.org/92/) to be published soon.And some samples of `es` and `de` datasets from [Common Voice](https://huggingface.co/datasets/common_voice) The script used for training can be found [less60wer.ipynb](./less60wer.ipynb) | 4c26773011fbd53a015deb8120326d06 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3927 - F1: 0.6863 | e264f6c1766627cbcaeed302fcbaff67 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1465 | 1.0 | 50 | 0.5838 | 0.4777 | | 0.505 | 2.0 | 100 | 0.4627 | 0.6393 | | 0.3783 | 3.0 | 150 | 0.3927 | 0.6863 | | 3ebdf1177a69a6505ea29bae68606603 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | deberta-v2-base-japanese-finetuned-emotion This model is a fine-tuned version of [ku-nlp/deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0465 - Accuracy: 0.9921 - F1: 0.9921 | ebc929d018a004f156e994577884224d |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0493 | 1.0 | 806 | 0.0273 | 0.9940 | 0.9940 | | 0.0106 | 2.0 | 1612 | 0.0465 | 0.9921 | 0.9921 | | f3785e6f79b8d61ea055f5e2c383d5fb |
apache-2.0 | ['ZEN', 'chinese'] | 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 | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | ZEN2 | 345M | 中文-Chinese | | f61adf003b5c285b27524d0c175e7441 |
apache-2.0 | ['ZEN', 'chinese'] | false | 模型信息 Model Information 我们与[ZEN团队](https://github.com/sinovation/ZEN)合作，使用我们的封神框架，开源发布了ZEN2模型。具体而言，通过引入无监督学习中提取的知识，ZEN通过N-gram方法学习不同的文本粒度信息。ZEN2使用大规模数据集和特殊的预训练策略对N-gram增强编码器进行预训练。下一步，我们将继续与ZEN团队一起探索PLM的优化，并提高下游任务的性能。 We open source and publicly release ZEN2 using our Fengshen Framework in collaboration with the [ZEN team](https://github.com/sinovation/ZEN). More precisely, by bringing together knowledge extracted by unsupervised learning, ZEN learns different textual granularity information through N-gram methods. ZEN2 pre-trains the N-gram-enhanced encoders with large-scale datasets and special pre-training strategies. In the next step, we continue with the ZEN team to explore the optimization of PLM and improve the performance on downstream tasks. | 525a3c91d030e5ef0239af80f3130ee2 |
apache-2.0 | ['ZEN', 'chinese'] | false | 下游效果 Performance **分类任务 Classification** | Model(Acc) | afqmc | tnews | iflytek | ocnli | cmnli | | :--------: | :-----: | :----: | :-----: | :----: | :----: | | Erlangshen-ZEN2-345M-Chinese | 0.741 | 0.584 | 0.599 | 0.788 | 0.80 | | Erlangshen-ZEN2-668M-Chinese | 0.75 | 0.60 | 0.589 | 0.81 | 0.82 | **抽取任务 Extraction** | Model(F1) | WEIBO(test) | Resume(test) | MSRA(test) | OntoNote4.0(test) | CMeEE(dev) | CLUENER(dev) | | :--------: | :-----: | :----: | :-----: | :----: | :----: | :----: | | Erlangshen-ZEN2-345M-Chinese | 65.26 | 96.03 | 95.15 | 78.93 | 62.81 | 79.27 | | Erlangshen-ZEN2-668M-Chinese | 70.02 | 96.08 | 95.13 | 80.89 | 63.37 | 79.22 | | 8b6ff966429e84aad1518d08593d3809 |
apache-2.0 | ['ZEN', 'chinese'] | false | 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有ZEN2相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of ZEN2 in [transformers library](https://github.com/huggingface/transformers), you can find the structure of ZEN2 and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ```python from fengshen.models.zen2.ngram_utils import ZenNgramDict from fengshen.models.zen2.tokenization import BertTokenizer from fengshen.models.zen2.modeling import ZenForSequenceClassification, ZenForTokenClassification pretrain_path = 'IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese' tokenizer = BertTokenizer.from_pretrained(pretrain_path) model_classification = ZenForSequenceClassification.from_pretrained(pretrain_path) model_extraction = ZenForTokenClassification.from_pretrained(pretrain_path) ngram_dict = ZenNgramDict.from_pretrained(pretrain_path, tokenizer=tokenizer) ``` 你可以从下方的链接获得我们做分类和抽取的详细示例。 You can get classification and extraction examples below. [分类 classification example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/fs_zen2_base_tnews.sh) [抽取 extraction example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/ner_zen2_base_ontonotes4.sh) | 2409ce131bc15c204a980c50c2fad742 |
apache-2.0 | ['ZEN', 'chinese'] | false | 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文： If you are using the resource for your work, please cite the our paper for this model: ```text @article{Sinovation2021ZEN2, title="{ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders}", author={Yan Song, Tong Zhang, Yonggang Wang, Kai-Fu Lee}, journal={arXiv preprint arXiv:2105.01279}, year={2021}, } ``` 如果您在您的工作中使用了我们的模型，也可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [overview paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ``` | e5f643cc34fef515d12ae1f1ac755799 |
apache-2.0 | ['generated_from_trainer'] | false | vacc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8424 - Accuracy: 0.8793 - F1: 0.9176 - Recall: 0.975 - Precision: 0.8667 | ad2bff4b85304ede347d50afd6f86054 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 40 | 0b6e4f0f50f8e53775a5902adcf7bbed |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion Embedding by ConflictX For SD 2.0 trained on 768x768 images from midjourney and other sources. Install by downloading the step embedding, and put it in the \embeddings folder Another themed one, this one is more focused on vibrant and sweet environments. Use keyword: CandyPunk Images:       | 52fb3cb55b8657761eebaa532dd8299d |
apache-2.0 | ['generated_from_keras_callback'] | false | khasrul-alam/banglabert-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.8513 - Train End Logits Accuracy: 0.0 - Train Start Logits Accuracy: 0.0 - Validation Loss: 5.8678 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 1 | 7171e1ea6c6a4ece096dec7e02c5c597 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 6ec2aac47c330190213d4b0f9fbf2887 |
apache-2.0 | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 5.9297 | 0.0 | 0.0208 | 5.9075 | 0.0 | 0.0 | 0 | | 5.8513 | 0.0 | 0.0 | 5.8678 | 0.0 | 0.0 | 1 | | c6ab7d93fda69b775038346411052748 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base960-english-phoneme_v2 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4069 - Cer: 0.0900 | b1d8c7abaf6a77ff24d479a70e9a0ffa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP | 9dbabc448b34945f2c2a6d75b3b73bd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.18 | 6.94 | 500 | 0.3118 | 0.0923 | | 0.2622 | 13.88 | 1000 | 0.4387 | 0.1218 | | 0.2145 | 20.83 | 1500 | 0.4441 | 0.1121 | | 0.1429 | 27.77 | 2000 | 0.4001 | 0.1045 | | 0.0927 | 34.72 | 2500 | 0.4692 | 0.1062 | | 0.0598 | 41.66 | 3000 | 0.3960 | 0.0971 | | 0.0356 | 48.61 | 3500 | 0.4069 | 0.0900 | | 4e5f09cd2d2be3b8943a59ee3e460071 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-target-glue-mnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8037 - Accuracy: 0.6427 | ac5c7db39088f3035f0338ad78569fad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0736 | 0.04 | 500 | 1.0266 | 0.4807 | | 1.0005 | 0.08 | 1000 | 0.9516 | 0.5605 | | 0.9517 | 0.12 | 1500 | 0.9140 | 0.5810 | | 0.9271 | 0.16 | 2000 | 0.9009 | 0.5921 | | 0.919 | 0.2 | 2500 | 0.8858 | 0.6014 | | 0.9125 | 0.24 | 3000 | 0.8740 | 0.6069 | | 0.8965 | 0.29 | 3500 | 0.8676 | 0.6134 | | 0.89 | 0.33 | 4000 | 0.8547 | 0.6193 | | 0.8754 | 0.37 | 4500 | 0.8516 | 0.6214 | | 0.8779 | 0.41 | 5000 | 0.8448 | 0.6220 | | 0.8698 | 0.45 | 5500 | 0.8396 | 0.6252 | | 0.8653 | 0.49 | 6000 | 0.8371 | 0.6287 | | 0.8692 | 0.53 | 6500 | 0.8304 | 0.6309 | | 0.8579 | 0.57 | 7000 | 0.8307 | 0.6301 | | 0.8528 | 0.61 | 7500 | 0.8151 | 0.6409 | | 0.8538 | 0.65 | 8000 | 0.8153 | 0.6381 | | 0.8451 | 0.69 | 8500 | 0.8264 | 0.6329 | | 0.8497 | 0.73 | 9000 | 0.8002 | 0.6464 | | 0.8401 | 0.77 | 9500 | 0.8125 | 0.6363 | | 0.8299 | 0.81 | 10000 | 0.7968 | 0.6464 | | 0.8343 | 0.86 | 10500 | 0.8037 | 0.6427 | | a0f06a4dbd3aa9049711d03cfc1b04f5 |
apache-2.0 | ['generated_from_trainer'] | false | sagemaker-distilbert-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - Accuracy: 0.9165 | 8fe399bf643364ef52b08cbaa6d73274 |
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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP | d7188c460a47e49b5d23d93f87fd1811 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9351 | 1.0 | 500 | 0.2469 | 0.9165 | | 413b1fc97bead21a05e97bdb9b02a16d |
apache-2.0 | ['translation'] | false | Model description This model is a T5 Transformer ([t5-small](https://huggingface.co/t5-small)) fine-tuned on 29,007 spanish and nahuatl sentences using 12,890 samples collected from the web and 16,117 samples from the Axolotl dataset. The dataset is normalized using 'sep' normalization from [py-elotl](https://github.com/ElotlMX/py-elotl). | 161ff2e9b27ab05d456b436b9d0980eb |
apache-2.0 | ['translation'] | false | Usage ```python from transformers import AutoModelForSeq2SeqLM from transformers import AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained('milmor/t5-small-spanish-nahuatl') tokenizer = AutoTokenizer.from_pretrained('milmor/t5-small-spanish-nahuatl') model.eval() sentence = 'muchas flores son blancas' input_ids = tokenizer('translate Spanish to Nahuatl: ' + sentence, return_tensors='pt').input_ids outputs = model.generate(input_ids) | 360540801f6a5966536ba687738001ba |
apache-2.0 | ['translation'] | false | Evaluation results The model is evaluated on 400 validation sentences. - Validation loss: 1.36 _Note: Since the Axolotl corpus contains multiple misalignments, the real Validation loss is slightly better. These misalignments also introduce noise into the training._ | de7431069aa04b582036e81a07d7e3eb |
apache-2.0 | ['translation'] | false | References - Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified Text-to-Text transformer. - Ximena Gutierrez-Vasques, Gerardo Sierra, and Hernandez Isaac. 2016. Axolotl: a web accessible parallel corpus for Spanish-Nahuatl. In International Conference on Language Resources and Evaluation (LREC). > Created by [Emilio Alejandro Morales](https://huggingface.co/milmor). | bab43e4526a833ffc57b2697a469dca7 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`. | 39788649bccd727b1534c2237ff597d5 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head") qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False) print(qap(question="国語",context="全学年にわたって小学校の国語の教科書に挿し絵>が用いられている")) ``` or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/)) ```py class TransformersUD(object): def __init__(self,bert): import os from transformers import (AutoTokenizer,AutoModelForQuestionAnswering, AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline) self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForQuestionAnswering.from_pretrained(bert) x=AutoModelForTokenClassification.from_pretrained if os.path.isdir(bert): d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger")) else: from transformers.utils import cached_file c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json")) d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c) s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json")) t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s) self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer, aggregation_strategy="simple") self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)] z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w) r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan) v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[] for i,t in enumerate(v): q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id] c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]]) b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c] with torch.no_grad(): d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]), token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b])) s,e=d.start_logits.tolist(),d.end_logits.tolist() for i in range(n): for j in range(n): m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: i=([p for s,e,p in w]+["root"]).index("root") j=i+1 if i<n else numpy.nanargmax(m[:,0]) m[0:j,0]=m[j+1:,0]=numpy.nan h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u=" | a1ef464349cc92ad4ab61da498c5eded |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` | 694190d559b405923b33fb6d59e1bf1d |
apache-2.0 | [] | false | *This repository provides a sharded version of the T0pp model that can be loaded in low-memory setups.* **Official repositories**: [Github](https://github.com/bigscience-workshop/t-zero) | [Hugging Face Hub](https://huggingface.co/bigscience/T0pp) | f6a72fdc146c3c9be28246cacad2244c |
apache-2.0 | [] | false | Model Description T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks. | 002d65a17ca83d1a37d7ad8bc7b00169 |
apache-2.0 | [] | false | Intended uses You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*. A few other examples that you can try: - *A is the son's of B's uncle. What is the family relationship between A and B?* - *Question A: How is air traffic controlled?<br> Question B: How do you become an air traffic controller?<br> Pick one: these questions are duplicates or not duplicates.* - *Is the word 'table' used in the same meaning in the two following sentences?<br><br> Sentence A: you can leave the books on the table over there.<br> Sentence B: the tables in this book are very hard to read.* - *Max: Know any good websites to buy clothes from?<br> Payton: Sure :) LINK 1, LINK 2, LINK 3<br> Max: That's a lot of them!<br> Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br> Max: I'll check them out. Thanks.<br><br> Who or what are Payton and Max referring to when they say 'them'?* - *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br> The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br> Which book is the leftmost book?* - *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.* | 6280a67fb97e82f5b907b26d348f0b34 |
apache-2.0 | [] | false | How to use We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks. |Model|Number of parameters| |-|-| |[T0](https://huggingface.co/bigscience/T0)|11 billion| |[T0p](https://huggingface.co/bigscience/T0p)|11 billion| |[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion| |[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion| |[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion| |[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion| Here is how to use the model in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp") model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp") inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`. **Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.** | 0a4ac44cf27fb50c82eef70269882232 |
apache-2.0 | [] | false | Training procedure T0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md | 2370943b6ff454369b6afa7a44ceda9e |
apache-2.0 | [] | false | lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective. At a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section. Training details: - Fine-tuning steps: 12'200 - Input sequence length: 1024 - Target sequence length: 256 - Batch size: 1'024 sequences - Optimizer: Adafactor - Learning rate: 1e-3 - Dropout: 0.1 - Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples) - Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length | 223ffb6a962c164a6318d6e7fa1bcc10 |
apache-2.0 | [] | false | Training data We trained different variants T0 with different mixtures of datasets. |Model|Training datasets| |--|--| |T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop<br>- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES<br>- Closed-Book QA: Hotpot QA*, Wiki QA<br>- Structure-To-Text: Common Gen, Wiki Bio<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum<br>- Topic Classification: AG News, DBPedia, TREC<br>- Paraphrase Identification: MRPC, PAWS, QQP| |T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:<br>- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag<br>- Extractive QA: SQuAD v2<br>- Closed-Book QA: Trivia QA, Web Questions| |T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):<br>- BoolQ<br>- COPA<br>- MultiRC<br>- ReCoRD<br>- WiC<br>- WSC| |T0_single_prompt|Same as T0 but only one prompt per training dataset| |T0_original_task_only|Same as T0 but only original tasks templates| |T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model| For reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page. *: We recast Hotpot QA as closed-book QA due to long input sequence length. | 7a9de8c3d66fe50e36afcc697d5cccbd |
apache-2.0 | [] | false | Evaluation data We evaluate our models on a suite of held-out tasks: |Task category|Datasets| |-|-| |Natural language inference|ANLI, CB, RTE| |Coreference resolution|WSC, Winogrande| |Word sense disambiguation|WiC| |Sentence completion|COPA, HellaSwag, Story Cloze| We also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench): - Code description task - Conceptual combinations - Hindu knowledge json - Known unknowns - Language identification - Logic grid puzzle task - Logical deduction - Common misconceptions - Movie dialog same or different - Novel concepts - Strategyqa - Formal fallacies syllogisms negation - VitaminC - Winowhy multiple choice | b3e96f3937c54fd8cac4fc5eb2c2adc1 |
apache-2.0 | [] | false | Limitations - The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html). - We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model. - Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text. | 652ebc85f1a655375a5bdda6e951c563 |
apache-2.0 | [] | false | Bias and fairness Even if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics: - Input: `Is the earth flat?` - Prediction: `yes` - Input: `Do vaccines cause autism?` - Prediction: `yes` - Input: `Complete this sentence: This man works as a` - Prediction: `Architect` - Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny` - Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex` - Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault` - Input: `what is something everyone hates, but you like?` - Prediction: `sex` - Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex` - Input: `What is your favorite "your mom" joke?` - Prediction: `Your mom is a slut` - Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy` Language models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases. To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts. <table> <tr> <td>Dataset</td> <td>Model</td> <td>Average (Acc.)</td> <td>Median (Acc.)</td> </tr> <tr> <td rowspan="10">CrowS-Pairs</td><td>T0</td><td>59.2</td><td>83.8</td> </tr> <td>T0p</td><td>57.6</td><td>83.8</td> <tr> </tr> <td>T0pp</td><td>62.7</td><td>64.4</td> <tr> </tr> <td>T0_single_prompt</td><td>57.6</td><td>69.5</td> <tr> </tr> <td>T0_original_task_only</td><td>47.1</td><td>37.8</td> <tr> </tr> <td>T0_3B</td><td>56.9</td><td>82.6</td> </tr> <tr> <td rowspan="10">WinoGender</td><td>T0</td><td>84.2</td><td>84.3</td> </tr> <td>T0p</td><td>80.1</td><td>80.6</td> <tr> </tr> <td>T0pp</td><td>89.2</td><td>90.0</td> <tr> </tr> <td>T0_single_prompt</td><td>81.6</td><td>84.6</td> <tr> </tr> <td>T0_original_task_only</td><td>83.7</td><td>83.8</td> <tr> </tr> <td>T0_3B</td><td>69.7</td><td>69.4</td> </tr> </table> To measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts. <table> <tr> <td rowspan="2">Model</td> <td rowspan="2">Subset</td> <td colspan="3">Average (Acc.)</td> <td colspan="3">Median (Acc.)</td> </tr> <tr> <td>Pro</td> <td>Anti</td> <td>Pro - Anti</td> <td>Pro</td> <td>Anti</td> <td>Pro - Anti</td> </tr> <tr> <td rowspan="2">T0</td><td>Type 1</td> <td>68.0</td><td>61.9</td><td>6.0</td><td>71.7</td><td>61.9</td><td>9.8</td> </tr> <td>Type 2</td> <td>79.3</td><td>76.4</td><td>2.8</td><td>79.3</td><td>75.0</td><td>4.3</td> </tr> </tr> <td rowspan="2">T0p</td> <td>Type 1</td> <td>66.6</td><td>57.2</td><td>9.4</td><td>71.5</td><td>62.6</td><td>8.8</td> </tr> </tr> <td>Type 2</td> <td>77.7</td><td>73.4</td><td>4.3</td><td>86.1</td><td>81.3</td><td>4.8</td> </tr> </tr> <td rowspan="2">T0pp</td> <td>Type 1</td> <td>63.8</td><td>55.9</td><td>7.9</td><td>72.7</td><td>63.4</td><td>9.3</td> </tr> </tr> <td>Type 2</td> <td>66.8</td><td>63.0</td><td>3.9</td><td>79.3</td><td>74.0</td><td>5.3</td> </tr> </tr> <td rowspan="2">T0_single_prompt</td> <td>Type 1</td> <td>73.7</td><td>60.5</td><td>13.2</td><td>79.3</td><td>60.6</td><td>18.7</td> </tr> </tr> <td>Type 2</td> <td>77.7</td><td>69.6</td><td>8.0</td><td>80.8</td><td>69.7</td><td>11.1</td> </tr> </tr> <td rowspan="2">T0_original_task_only</td> <td>Type 1</td> <td>78.1</td><td>67.7</td><td>10.4</td><td>81.8</td><td>67.2</td><td>14.6</td> </tr> </tr> <td> Type 2</td> <td>85.2</td><td>82.3</td><td>2.9</td><td>89.6</td><td>85.4</td><td>4.3</td> </tr> </tr> <td rowspan="2">T0_3B</td> <td>Type 1</td> <td>82.3</td><td>70.1</td><td>12.2</td><td>83.6</td><td>62.9</td><td>20.7</td> </tr> </tr> <td> Type 2</td> <td>83.8</td><td>76.5</td><td>7.3</td><td>85.9</td><td>75</td><td>10.9</td> </tr> </table> | bd16639e0535769ce915012c92383bdf |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @misc{sanh2021multitask, title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush}, year={2021}, eprint={2110.08207}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` | 81a50208c0bc3db9ce03a3a8d20772b8 |
mit | ['generated_from_trainer'] | false | CR_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3728 - Accuracy: 0.9333 | 444aa71403d5ab494c18874d8e0a2d23 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6307 | 0.33 | 50 | 0.4608 | 0.66 | | 0.3468 | 0.66 | 100 | 0.3195 | 0.8933 | | 0.2359 | 0.99 | 150 | 0.2952 | 0.9 | | 0.1786 | 1.32 | 200 | 0.2839 | 0.92 | | 0.2581 | 1.66 | 250 | 0.2955 | 0.9267 | | 0.231 | 1.99 | 300 | 0.2864 | 0.9133 | | 0.1262 | 2.32 | 350 | 0.4320 | 0.8933 | | 0.1935 | 2.65 | 400 | 0.2874 | 0.9133 | | 0.1646 | 2.98 | 450 | 0.3581 | 0.9133 | | 0.1151 | 3.31 | 500 | 0.3666 | 0.92 | | 0.1184 | 3.64 | 550 | 0.3496 | 0.9267 | | 0.1089 | 3.97 | 600 | 0.3655 | 0.9267 | | 0.0969 | 4.3 | 650 | 0.3607 | 0.9267 | | 0.0988 | 4.64 | 700 | 0.3707 | 0.9333 | | 0.0597 | 4.97 | 750 | 0.3728 | 0.9333 | | c9eacf1b5277fd11334e6f75ee31d26f |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5537 - Rouge1: 0.1417 - Rouge2: 0.0517 - Rougel: 0.1173 - Rougelsum: 0.1172 - Gen Len: 19.0 | 326b06448a636546b7da2eb542c50d12 |
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 | 62 | 2.7255 | 0.1315 | 0.0434 | 0.1091 | 0.109 | 19.0 | | No log | 2.0 | 124 | 2.6129 | 0.1351 | 0.0458 | 0.1121 | 0.112 | 19.0 | | No log | 3.0 | 186 | 2.5659 | 0.1402 | 0.0498 | 0.1161 | 0.1161 | 19.0 | | No log | 4.0 | 248 | 2.5537 | 0.1417 | 0.0517 | 0.1173 | 0.1172 | 19.0 | | e97a18d09f30e52db024f3c63429e933 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the arckt concept trained by patrickfleith on the patrickfleith/dreambooth-hackathon-images-arckt dataset. This is a Stable Diffusion model fine-tuned on the arckt (Ariane 5 rocket) concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of arckt rocket** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 702aeb71acef41ea14563bd2c4e54cd9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner 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: 0.0614 - Precision: 0.9288 - Recall: 0.9388 - F1: 0.9338 - Accuracy: 0.9840 | 3373692e09a9aeea6547653de34c9b50 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2456 | 1.0 | 878 | 0.0683 | 0.9151 | 0.9223 | 0.9187 | 0.9814 | | 0.0542 | 2.0 | 1756 | 0.0609 | 0.9227 | 0.9335 | 0.9281 | 0.9829 | | 0.0293 | 3.0 | 2634 | 0.0614 | 0.9288 | 0.9388 | 0.9338 | 0.9840 | | 813a0cbffec397fd83e795952753a1c4 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v2-base Model Card This model card focuses on the model associated with the Stable Diffusion v2-base model, available [here](https://github.com/Stability-AI/stablediffusion). The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`.  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base | 851fb116bded94c53884c8d9bb29808f |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler): ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler import torch model_id = "stabilityai/stable-diffusion-2-base" | 04b3cf42ee349745f580a2504564cbb1 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) | c9a9fbcedc198c3eeea2464a1faa1f30 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant | c9fd9b5ea9606855eae76d0699b4fe9c |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/bart-large-squad-qg-ae` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 05b9ddd9399e45901a7c799115099d2b |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | c6c3ba154fab80e4f51c8b9b1f1e9538 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-large-squad-qg-ae") | cbf0c0e5b6fc4e70e044c096ba753421 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.88 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 59.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 43.51 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 33.77 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 26.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 27.32 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 65.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 54.27 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 93.36 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.61 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.68 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 63.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 94.05 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 65.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:---------------------------------------------------------------| | AnswerExactMatch | 59.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | AnswerF1Score | 70.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | BERTScore | 91.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 67.03 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 64.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 61.73 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 59.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 42.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 82.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 69.5 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | dba56873bfb8557aa68624141f5a2f72 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 64 - lr: 1e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-squad-qg-ae/raw/main/trainer_config.json). | 11c1c9137aac1a87de7b6f382f001c61 |
apache-2.0 | ['generated_from_trainer'] | false | bart-large-finetuned-parth This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2530 - Rouge1: 40.8179 - Rouge2: 29.1558 - Rougel: 38.4554 - Rougelsum: 41.154 - Gen Len: 20.0 | ce9655c068b89651ccc68b20fb5bc596 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - label_smoothing_factor: 0.1 | 55ee11d4e8f67bfe34876121f115eab6 |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-16-1 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6804 - Accuracy: 0.5497 | 5514385f9997a86a92451ab375aa2546 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7086 | 1.0 | 7 | 0.7176 | 0.2857 | | 0.6897 | 2.0 | 14 | 0.7057 | 0.2857 | | 0.6491 | 3.0 | 21 | 0.6582 | 0.8571 | | 0.567 | 4.0 | 28 | 0.4480 | 0.8571 | | 0.4304 | 5.0 | 35 | 0.5465 | 0.7143 | | 0.0684 | 6.0 | 42 | 0.5408 | 0.8571 | | 0.0339 | 7.0 | 49 | 0.6501 | 0.8571 | | 0.0082 | 8.0 | 56 | 0.9152 | 0.8571 | | 0.0067 | 9.0 | 63 | 2.5162 | 0.5714 | | 0.0045 | 10.0 | 70 | 1.1136 | 0.8571 | | 0.0012 | 11.0 | 77 | 1.1668 | 0.8571 | | 0.0007 | 12.0 | 84 | 1.2071 | 0.8571 | | 0.0005 | 13.0 | 91 | 1.2310 | 0.8571 | | 0.0006 | 14.0 | 98 | 1.2476 | 0.8571 | | 06773968e8fc2d79d527e7df2882d68e |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | fine-tuned_ar-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on the tatoeba_mt dataset. It achieves the following results on the evaluation set: - Loss: 0.8464 - Bleu: 51.8158 | a8ac221cb4a2cd7ef20852eb756925bb |
mit | ['generated_from_keras_callback'] | false | Deep98/Heresy-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2244 - Train End Logits Accuracy: 0.9479 - Train Start Logits Accuracy: 0.9062 - Validation Loss: 0.4860 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | dc6b0c46bb9682e20cec7f31d9795321 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2244 | 0.9479 | 0.9062 | 0.4860 | 0.6667 | 1.0 | 0 | | c582230222a9866324b350d5469831a6 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-zh This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.9338 - Bleu: 40.6658 | 2cdcda1d0a4a1e51a2d923811e7b15e1 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_gender_male-10_female-0_s530 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | b4a99894861ebe825f46d0d60f5aa846 |
apache-2.0 | ['translation'] | false | eng-gle * source group: English * target group: Irish * OPUS readme: [eng-gle](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gle/README.md) * model: transformer-align * source language(s): eng * target language(s): gle * 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/eng-gle/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gle/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gle/opus-2020-06-17.eval.txt) | be1dd9fde350601b467fca759c9b8ee5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-gle - source_languages: eng - target_languages: gle - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gle/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'ga'] - src_constituents: {'eng'} - tgt_constituents: {'gle'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gle/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gle/opus-2020-06-17.test.txt - src_alpha3: eng - tgt_alpha3: gle - short_pair: en-ga - chrF2_score: 0.593 - bleu: 37.5 - brevity_penalty: 1.0 - ref_len: 12200.0 - src_name: English - tgt_name: Irish - train_date: 2020-06-17 - src_alpha2: en - tgt_alpha2: ga - prefer_old: False - long_pair: eng-gle - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | eef74322cd55752765c770a0a2320a14 |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | NOTE: This model is only pretrained on TeaBReaC, and not on any real QA dataset. from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from digit_tokenization import enable_digit_tokenization | 826e9082476133d5956a09eac856a430 |
apache-2.0 | ['generated_from_trainer', 'dutch', 'whisper-event'] | false | whisper-small-nl This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3034 - Wer: 14.5354 | 14d28c6311ae0191e72da1371982df66 |
apache-2.0 | ['generated_from_trainer', 'dutch', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - 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_precision_training: Native AMP | fa825cd4cd3cf67165e7610a530ba0d0 |
apache-2.0 | ['generated_from_trainer', 'dutch', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.2045 | 2.49 | 1000 | 0.3194 | 16.1628 | | 0.0652 | 4.97 | 2000 | 0.3425 | 16.3672 | | 0.0167 | 7.46 | 3000 | 0.3915 | 15.8187 | | 0.0064 | 9.95 | 4000 | 0.4190 | 15.7298 | | 0.1966 | 2.02 | 5000 | 0.3298 | 15.0881 | | 0.1912 | 4.04 | 6000 | 0.3266 | 14.8764 | | 0.1008 | 7.02 | 7000 | 0.3261 | 14.8086 | | 0.0899 | 9.04 | 8000 | 0.3196 | 14.6487 | | 0.1126 | 12.02 | 9000 | 0.3283 | 14.5894 | | 0.1071 | 14.04 | 10000 | 0.3034 | 14.5354 | | 67dc374270bf1d4572a7d37ed6ab0a99 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_left_headline_conc_overfit This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8306 - Validation Loss: 3.0281 - Epoch: 19 | bb7a2ac7c399b45f15294f5f6abe59d2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -929, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | d6c3de77d4476c1318f0cd4aa61d39a7 |
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