license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0267 | 1.0 | 878 | 0.0754 | 0.9301 | 0.9282 | 0.9291 | 0.9829 | | 0.0141 | 2.0 |... | c9d18beb23ccaf59d3b6a527cfcf1acd |
apache-2.0 | ['pegasus', 'paraphrasing', 'seq2seq'] | false | Model in Action 🚀 ``` import torch from transformers import PegasusForConditionalGeneration, PegasusTokenizer model_name = 'tuner007/pegasus_paraphrase' torch_device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = PegasusTokenizer.from_pretrained(model_name) model = PegasusForConditionalGeneration.from_p... | 56ef9bd54230f7a7646d7165eb08aa87 |
apache-2.0 | ['pegasus', 'paraphrasing', 'seq2seq'] | false | output: ['The test of your knowledge is your ability to convey it.', 'The ability to convey your knowledge is the ultimate test of your knowledge.', 'The ability to convey your knowledge is the most important test of your knowledge.', 'Your capacity to convey your knowledge is the ultimate test of it.', 'The test ... | 05d748e168ffbe77366473208245d786 |
apache-2.0 | ['translation'] | false | tha-eng * source group: Thai * target group: English * OPUS readme: [tha-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tha-eng/README.md) * model: transformer-align * source language(s): tha * target language(s): eng * model: transformer-align * pre-processing: normalization + Sentenc... | efe2538397562a59bc10ae446dcb771f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tha-eng - source_languages: tha - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tha-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['th', 'en'] - src_constituents: {'tha'} - tgt_const... | 366f3c3282b552db03839a2228f78957 |
apache-2.0 | [] | false | BERT Large model HPU configuration This model only contains the `GaudiConfig` file for running the [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enable... | 99f5db6ffb0d22b4f3c7cd2b515a23e8 |
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... | 4463d20b76e4e40b2f75709543f382dc |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | This model is a fine-tuned diffusion model for unconditional image generation of animefaces. Even after fine-tuning the diffusion model for 10 epochs the generated images are still cursed... 💀. Maybe more epochs would help?  | 5d11365c49521a129c792cbf23914a5d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab 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: 0.5255 - Wer: 0.3330 | 10902816063abc35dc76c51a3ff53906 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5942 | 1.0 | 500 | 2.3849 | 1.0011 | | 0.9765 | 2.01 | 1000 | 0.5907 | 0.5202 | | 0.4424 | 3.01 | 1500 | 0.4547 | 0.466... | e407375cd602df16f8bbc3237544f84c |
apache-2.0 | ['generated_from_trainer'] | false | berttest2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0674 - Precision: 0.9138 - Recall: 0.9325 - F1: 0.9230 - Accuracy: 0.9823 | 792f8557aea95a8143dae1cad57e7f54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0869 | 1.0 | 1756 | 0.0674 | 0.9138 | 0.9325 | 0.9230 | 0.9823 | | db1940494264bf9a1a426f45120d01ea |
mit | [] | false | Miko 3 robot on Stable Diffusion This is the `<miko-3>` 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 t... | 7f91577212ac73258fa12193ea77005b |
apache-2.0 | ['translation'] | false | opus-mt-swc-en * source languages: swc * target languages: en * OPUS readme: [swc-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/swc-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 26a9baf9ec7f9766d1dcfd4f4c920d40 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-qgsquad-qgen This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the qg_squad dataset. It achieves the following results on the evaluation set: - Loss: 0.4039 - Rouge4 Precision: 0.0931 - Rouge4 Recall: 0.0834 - Rouge4 Fmeasure: 0.0843 | 67ffbde7d6d95472b0c9743788e6110c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge4 Precision | Rouge4 Recall | Rouge4 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.4325 | 1.0 | 4733 | 0.3960 | 0.0984 | 0.0867 | 0.08... | 234b1e9cc48f0c23e532b6c2f6baa978 |
creativeml-openrail-m | ['text-to-image'] | false | model by IR1763 This your the Stable Diffusion model fine-tuned the Pranav concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks person** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.researc... | 64ae124e1eaff941edd07e415ffe21a7 |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Model description This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**. Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/clo... | 671f69547220b20c7427c29259c97a95 |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | How to use? 1. Download the model by hugging face transformers. ```python from transformers import BertTokenizer, BertForMaskedLM, pipeline tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-scibert-cloth") csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-scibert-cloth") ``` 2. Create a ... | 38ceb6ec9ec0c179bd70813df471f4cf |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Training hyperparameters The following hyperparameters were used during training: - Pre-train language model: [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) - Optimizer: adam - Learning rate: 0.0001 - Max length of input: 64 - Batch size: 64 - Epoch: 1 - Device: NVIDIA® T... | f340ac43b214a958cfb2f94b75c04629 |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Testing The evaluations of this model as a Candidate Set Generator in CDGP is as follows: | P@1 | F1@3 | F1@10 | MRR | NDCG@10 | | ---- | ---- | ----- | ----- | ------- | | 8.10 | 9.13 | 12.22 | 19.53 | 28.76 | | 0760fb4f837576aa1a666551fcd4034b |
mit | ['bert', 'cloze', 'distractor', 'generation'] | false | Candidate Set Generator | Models | CLOTH | DGen | | ----------- | ----------------------------------------------------------------------------------- | -------... | 36355d3b57c613711ee623fbbb873d22 |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/aesrc2020/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_aesrc2020_asr_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | a6daab74bc5c4da74e69f341fe443976 |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | Environments - date: `Sat Aug 20 06:55:57 EDT 2022` - python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1` - Git hash: `c892feb2ba248c85b683bf3cdef6c8f7ce85449a` - Commit date: `Thu Aug 18 11:54:56 2022 -0400` | 34e2262178b9680537a30aaef3a0790e |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_wnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3455 - Accuracy: 0.5634 | 7bf3eac0c60c2f188ec62729b51a0d6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.347 | 1.0 | 5 | 0.3455 | 0.5634 | | 0.3467 | 2.0 | 10 | 0.3458 | 0.5634 | | 0.3466 | 3.0 | 15 | 0.3459 | 0.... | c160542af1f82ac6e9cddbf39a606196 |
mit | [] | false | Introduction **MERT-v0** is a completely unsupervised model trained on 1000 hour music audios. Its architecture is similar to the [HuBERT model](https://huggingface.co/docs/transformers/model_doc/hubert), but it has been specifically designed for music through the use of specialized pre-training strategies. It is S... | 80558a947b0984919dde68d01118ecbf |
mit | [] | false | load demo audio and set processor dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft") | 3f0455ad569ebe6f523d6387721f8b09 |
mit | [] | false | audio file is decoded on the fly inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs, output_hidden_states=True) | a527d669790c5c82251a6582e5d7e2c7 |
mit | [] | false | each layer performs differently in different downstream tasks, you should choose empirically all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() print(all_layer_hidden_states.shape) | 4dee30d8588930d662ad290ff09bd5cb |
mit | [] | false | you can even use a learnable weighted average representation aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1) weighted_avg_hidden_states = aggregator(time_reduced_hidden_states.unsqueeze(0)).squeeze() print(weighted_avg_hidden_states.shape) | 478b5a8a49347b4d9d0f84ada82dc16b |
mit | [] | false | Citation ```shell @article{li2022large, title={Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning}, author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others}, yea... | fce2998ee26871059fd40fb67d715d93 |
apache-2.0 | ['translation'] | false | zho-nld * source group: Chinese * target group: Dutch * OPUS readme: [zho-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-nld/README.md) * model: transformer-align * source language(s): cmn cmn_Bopo cmn_Hani cmn_Hira cmn_Kana cmn_Latn * target language(s): nld * model: transformer-a... | d71dd72007b6a1b48f3aba41c5cd3e53 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-nld - source_languages: zho - target_languages: nld - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-nld/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'nl'] - src_constituents: {'cmn_Hans', 'nan', ... | 173c1b866585b3d1e7eb5b98f6a86fb8 |
apache-2.0 | ['translation'] | false | opus-mt-fi-lg * source languages: fi * target languages: lg * OPUS readme: [fi-lg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-lg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | f4c548d66ac8dfc7c67019922e7d1f74 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1656 - F1: 0.8589 | 52a8bbdf48f2de8bffdc6e4865bf5fcc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 | | 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 | | 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 | ... | 95bfc0f2e4b5424632dcdbfa6e6daa39 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/5-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5941 - Train End Logits Acc... | a2bc16e0a52022a1392a73b1418f7ab7 |
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': 132, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | c8237f91afc4b31f26dc0f0b18a40763 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 60d721d02a8e6b4f80ac157bdbd69c5c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-emotion-finetuned 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: 0.1518 - Acc: 0.935 - F1: 0.9350 | 631e2e146aeb0a91bc7d547d943c2b65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----:|:------:| | 0.1734 | 1.0 | 250 | 0.1624 | 0.928 | 0.9279 | | 0.1187 | 2.0 | 500 | 0.1518 | 0.935 | 0.9350 | | 4f6efd7e7db874f0373e1fc9067406ba |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Kn - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1398 - Wer: 23.8167 | d0dbdfa140a2daf8cc1b4b5e03836693 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4126 | 0.1 | 500 | 2.2797 | 127.2639 | | 0.2099 | 0.1 | 1000 | 0.1774 | 28.2494 | | 0.1736 | 0.2 | 1500 | 0.1565 | 27... | 566fca7d297a1869d887b42b378c1465 |
mit | ['generated_from_keras_callback'] | false | Andaf/chatbot-trvlk-finetuned-squad This model is a fine-tuned version of [cahya/bert-base-indonesian-522M](https://huggingface.co/cahya/bert-base-indonesian-522M) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5335 - Validation Loss: 6.4566 - Epoch: 1 | 91c41aee4f471f6f780e5218a5e21c22 |
mit | ['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': 14444, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 2dfc01eeaeba60a21fc287eeeee24b11 |
apache-2.0 | ['classification', 'zero-shot'] | false | Erlangshen-UniMC-MegatronBERT-1.3B-Chinese - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) - API: [Fengshen-OpenAPI](https://fengs... | 4a643cbef02d8ff07ece93ef9c8adcc8 |
apache-2.0 | ['classification', 'zero-shot'] | 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 | MegatronBERT | 1.3B | Chinese | | 58119c9a8bed844f10d54828414f8fee |
apache-2.0 | ['classification', 'zero-shot'] | false | 模型信息 Model Information 我们为零样本学习者提出了一种与输入无关的新范式，从某种意义上说，它与任何格式兼容并适用于一系列语言任务，例如文本分类、常识推理、共指解析、情感分析。我们的方法将零样本学习转化为多项选择任务，避免常用的大型生成模型（如 FLAN）中的问题。它不仅增加了模型的泛化能力，而且显着减少了对参数的需求。我们证明了这种方法可以在通用语言基准上取得最先进的性能，并在自然语言推理和文本分类等任务上产生令人满意的结果。更多详细信息可以参考我们的[论文](https://arxiv.org/abs/2210.08590)或者[GitHub](https://github.com/IDEA-CCNL/Fe... | 0a058315c8890770455d77ee34602c17 |
apache-2.0 | ['classification', 'zero-shot'] | false | 下游效果 Performance **Few-shot** | Model | eprstmt | csldcp | tnews | iflytek | ocnli | bustm | chid | csl | wsc | Avg | |------------|------------|----------|-----------|----------|-----------|-----------|-----------|----------|-----------|-----------| | [FineTuning](https:/... | d21825bb484242db98e59287373d6a11 |
apache-2.0 | ['classification', 'zero-shot'] | false | 使用 Usage ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git cd Fengshenbang-LM pip install --editable . ``` ```python3 import argparse from fengshen.pipelines.multiplechoice import UniMCPipelines total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UniMCPipelines.piplines_args(total_... | 977815a209881044cb1a19a9e7535ea3 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | legal-bert-base-uncased-finetuned-RRamicus This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1520 | 99081a050049b5afae92a907d5f6c2b1 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.021 | 1.0 | 1118 | 1.3393 | | 1.2272 | 2.0 | 2236 | 1.2612 | | 1.2467 | 3.0 | 3354 | 1.2403 | | 1.2149 | 4.0 | 4472 | 1.2276 ... | 1be1a7021600a8e51dcf28c6b51d23bd |
other | ['stable-diffusion', 'text-to-image'] | false | ご利用の際は下記のライセンス内容を十分にご確認ください。 If you can read English, please refer [here](https://huggingface.co/nakayama/DeDeDe/blob/main/README-en.md). DeDeDeはアニメ調の人物を出力しやすいように調整されたStable Diffusionモデルです。 ベースモデルの[DreamLike Diffusion 1.0](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0)へ[Trinart Characters v2 De... | d32351272a20ec289f0813c0b631c153 |
other | ['stable-diffusion', 'text-to-image'] | false | 例 <img src="https://huggingface.co/nakayama/DeDeDe/resolve/main/img/image01.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece, 8k))), detailed anime style of anime 1girl bust shot sitting and dipping in river and wetty wearing white transparent onepiece dress with detailed wavy pink hair pi... | bef88af01c1fae76c7171feb19c018f2 |
other | ['stable-diffusion', 'text-to-image'] | false | マージ・学習手順について 以下、モデル横に記載されている記号列は、Automatic1111 Webui コミットハッシュ c98cb0f8ecc904666f47684e238dd022039ca16f 時点での、モデル選択時に記載されているckptのハッシュ値です。 1. Dreamlike Diffusion 1.0にTrinart Derridaを差分マージする | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Merge Name | | --- | --- | --- | --- | --- | | Add Diffe... | d72994ebfb92925eef68b7a085de1fb2 |
other | ['stable-diffusion', 'text-to-image'] | false | DeDeDe_ip2p_0.7_0.8.ckpt/DeDeDe_ip2p_0.7_1.0.ckpt [Instruct pix2pix](https://huggingface.co/timbrooks/instruct-pix2pix)モデルから[タスクベクトル](https://zenn.dev/discus0434/articles/ef418a8b0b3dc0)を抽出して加算したモデルです。 それぞれDeDeDe 0.8/Instruct Pix2Pix 0.7、DeDeDe 1.0/Instruct Pix2Pix 0.7の大きさとなります。 以下はInstruct Pix2Pixから継承したライセンスと... | 9ac53bb200f258946b7712df9b2e3e7a |
other | ['stable-diffusion', 'text-to-image'] | false | ライセンスについて 当モデルはDreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0の影響下にあるため、上記モデルにおける**修正された**CreativeML OpenRAIL-M licenseが適用されます。 以下はDeepLで翻訳された修正分の日本語訳となりますが、解釈において優先される言語は英語となります。 - **あなたが収入や寄付を得る、または得る予定のウェブサイト/アプリ/その他で、このモデルやその派生物をホストしたり使用したりすることはできません。もしそうしたいのなら、contact@dreamlike.art までメールしてください。** - **... | 736feacc62add6a08591d886b50b072b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6150 - Matthews Correlation: -0.0293 | 27bebbe1ce09854b03dd44021c552b64 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6207 | 1.0 | 34 | 0.6165 | 0.0 | | 0.6034 | 2.0 | 68 | 0.6150 | -0.0293 | | 0.5... | e1acf201740d2366b8354543b5947699 |
mit | ['distilbert', 'pytorch', 'text-classification', 'mobile app descriptions', 'playstore'] | false | Model description DistilBERT is a transformer model, smaller and faster than BERT, which was pre-trained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. The [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model is fine-tuned to classify an mobile app... | 244e203e4a37da8ae5c92d3a6b990e43 |
mit | ['distilbert', 'pytorch', 'text-classification', 'mobile app descriptions', 'playstore'] | false | Fine-tuning The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best evaluation f1 score achieved by the model was 0.9034534096919489, found af... | 759eda9ba3b21814087c287638a21a57 |
mit | ['distilbert', 'pytorch', 'text-classification', 'mobile app descriptions', 'playstore'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app") model = AutoModelForSequenceClassification.from_pretrained("nsi319/distilbert-base-uncased-finetuned-app") classifier ... | 01d2f884451bbf4001dce1b9e068a65b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5026 - Matthews Correlation: 0.4097 | 1f4e4488dbf36bb3aa23a544ed608e66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5335 | 1.0 | 535 | 0.5026 | 0.4097 | | 103065a4b2124d07bac3298dfae595c7 |
mit | [] | false | all rings albuns on Stable Diffusion This is the `<rings-all-albuns>` 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. ... | 5f3fc0dec9e651475b15fa11c94a6b03 |
mit | [] | false | tela lenca on Stable Diffusion This is the `<tela-lenca>` 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... | b563e78c7995c06cf360f6d896c9bd27 |
agpl-3.0 | ['generated_from_trainer'] | false | XLMR-ENIS-finetuned-ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0907 - Precision: 0.8666 - Recall: 0.8511 - F1: 0.8588 - Accuracy: 0.9834 | 63477b655cd8126f8c8227ee4cfdbc4d |
agpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0573 | 1.0 | 2904 | 0.0961 | 0.8543 | 0.8134 | 0.8334 | 0.9806 | | 0.0314 | 2.0 |... | 1ad0013de08ca706d9c168d823ed8566 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | e481c4a9ad0a0ec56a351e2fb5298a2f |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649934336}, 'generation': {'batch_size': 128, 'every_n_steps': 512, 'force_call_on': [12589], 'metrics_con... | 91dcedd17a6ed4f632c009c92c152ce1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'pt'] | false | wavlm-large-CORAA-pt-cv7 This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-large-CORAA-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2546 - Wer: 0.2261 | 4c6520db90f869db4150f30372d8a7cb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'pt'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | eeaec8bbae50a8e5dca0f58e98ea309e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'pt'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6029 | 0.13 | 100 | 0.3679 | 0.3347 | | 0.5297 | 0.26 | 200 | 0.3516 | 0.3227 | | 0.5134 | 0.39 | 300 | 0.3327 | 0.3167 | |... | d3869d90f7428062ee238728d7e44a74 |
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 the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1078 - Precision: 0.8665 - Recall: 0.8817 - F1: 0.8740 - Accuracy: 0.9717 | 0e4c758c81cf6f7da212f1a1b3ebea0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 220 | 0.0993 | 0.8511 | 0.8780 | 0.8643 | 0.9721 | | No log | 2.0 |... | 9944b8179a7ad8fb074d9e1b4eace92a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2354 - Accuracy: 0.917 - F1: 0.9171 | 3125ec078735d653517bacee82aa4a65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8588 | 1.0 | 250 | 0.3318 | 0.904 | 0.9013 | | 0.2586 | 2.0 | 500 | 0.2354 | 0.917 | 0.9171 | | 6fea5e7e06d58b5aaa71da798cc4c99c |
mit | ['nr', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_nbl_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_nbl_roberta") ``` | e02cd7e1c7d287ca4bf85de27fb9ef02 |
mit | ['generated_from_trainer'] | false | gpt-expt-sp-v3-K-200-9-mixed 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: 0.0470 | 2da74e58f964c8d738f73689456b8d4b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | 32e868af3de8849d54742a625e5057a5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:------:|:---------------:| | 0.4566 | 12.75 | 5000 | 0.0648 | | 0.0684 | 25.51 | 10000 | 0.0535 | | 0.058 | 38.26 | 15000 | 0.0505 | | 0.0545 | 51.02 | 2000... | aa3e184cd9fd8977a01e1e5d5e6d0870 |
apache-2.0 | ['abusive text classification'] | false | ```py from transformers import pipeline model_path = 'marianna13/xlm-roberta-fine-tuned-on-russian-abusive-language' id2label = { 0:'неопасный тескт', 1:'опасный тескт' } label2id = { 'неопасный тескт':0, 'опасный тескт':1 } config = AutoConfig.from_pretrained(model_path, id2label=id2label, label2... | 4f59a197b64cc0275b693ebc645529cf |
apache-2.0 | ['translation'] | false | Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/vi-en") tokenizer = AutoTokenizer.from_pretrained("CLAck/vi-en") sentence = your_vietnamese_sentence | 0b76a976708659f5b45b30fc059bf3b2 |
apache-2.0 | ['translation'] | false | This token is needed to identify the source language input_sentence = "<2vi> " + sentence translated = model.generate(**tokenizer(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` | 80e573e66b23e759ccf823a36713be0b |
mit | [] | false | Exodus-Styling on Stable Diffusion This is the `<Exouds-Style>` 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 ca... | 781a5ff6daa840fbe9082764a7424f3a |
apache-2.0 | ['tapas', 'sequence-classification'] | false | TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact) This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model... | 2db9cff1c3b501b9c976b8b5f942d885 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | This model was trained by ftshijt using aishell3/tts1 recipe in <a href="https://github.com/espnet/espnet/">espnet</a>. <p> </p> <ul> <li><strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre></li> <li><strong>Evaluate in the recipe</strong><pre> ... | 7e058bc64c2e1e5f882a3b07b3b0df55 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-target-glue-cola This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc](https://huggingface.co/muhtasham/small-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5250 - Matthews Correlation: 0.3249 | dfc105fe511414469c1981b39051a42d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5316 | 1.87 | 500 | 0.6534 | 0.2440 | | 0.3404 | 3.73 | 1000 | 0.7046 | 0.3402 | | 0.2... | 455fc02ec4aa68b9d8e3175ffefea7db |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s295 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | b182b8c30b87e5ee32b7652ccde92fed |
apache-2.0 | ['translation'] | false | opus-mt-en-ml * source languages: en * target languages: ml * OPUS readme: [en-ml](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ml/README.md) * dataset: opus+bt+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt+bt-2020-04-28.z... | a3c20e4c99a20b868a7f396ecc533f0d |
other | ['stable-diffusion', 'text-to-image'] | false | Cool Japan Diffusion 2.1.0 Model Card  [注意事项。从2023年1月10日起，中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告） English version is [here](README_en.md). | f1c1e4f760af3a54db072e98979a325a |
other | ['stable-diffusion', 'text-to-image'] | false | 使い方 手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/alfredplpl/cool-japan-diffusion-2-1-0)をお使いください。 詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2022/12/30/102636)にかかれています。 モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-0/resolve/main/v2-1-0.ckpt)からダウンロードできます。 以下、一般的なモデルカ... | 17290243708292837d70fee067040eb1 |
mit | ['generated_from_trainer'] | false | ECHR_test_2 This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the lex_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2487 - Macro-f1: 0.4052 - Micro-f1: 0.5660 | 0cb909b15b4ec1efae2bffab4edb9e32 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | c0b150c0f2487076fc2fabcb0fff2e79 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.2056 | 0.44 | 500 | 0.2846 | 0.3335 | 0.4763 | | 0.1698 | 0.89 | 1000 | 0.2487 | 0.4052 | 0.5660 | | f0853e367059dbfb453ec1b318aa390a |
apache-2.0 | ['Twitter', 'Multilingual'] | false | TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations [](http://makeapullrequest.com) [](https://arxiv.org/abs/2209.07562) ... | 8f1c534561ef9c35999619cae565f733 |
apache-2.0 | ['Twitter', 'Multilingual'] | false | Overview TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich soci... | 638bc0e9ba0ae425e57445135c062fdb |
apache-2.0 | ['Twitter', 'Multilingual'] | false | 1. Pretrained Models We initially release two pretrained TwHIN-BERT models (base and large) that are compatible wit the [HuggingFace BERT models](https://github.com/huggingface/transformers). | Model | Size | Download Link (🤗 HuggingFace) | | ------------- | ------------- | --------- | | TwHIN-BERT-base | 280M p... | 84e3be96f6e8577fc87ab4c5a47a4472 |
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