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 | 1756 | 0.0698 | 0.9279 | 0.9395 | 0.9336 | 0.9841 | | 0.0084 | 3.0 | 2634 | 0.0731 | 0.9331 | 0.9432 | 0.9381 | 0.9851 | | 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_pretrained(model_name).to(torch_device) def get_response(input_text,num_return_sequences,num_beams): batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device) translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5) tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True) return tgt_text ``` | 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 of your knowledge is your ability to communicate it.', 'Your capacity to convey your knowledge is the ultimate test of your knowledge.', 'Your capacity to convey your knowledge to another is the ultimate test of your knowledge.', 'Your capacity to convey your knowledge is the most important test of your knowledge.', 'The test of your knowledge is how well you can convey it.', 'Your capacity to convey your knowledge is the ultimate test.'] ``` > Created by [Arpit Rajauria](https://twitter.com/arpit_rajauria) [](https://twitter.com/arpit_rajauria) | 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 + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tha-eng/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tha-eng/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tha-eng/opus-2020-06-17.eval.txt) | 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_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tha-eng/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tha-eng/opus-2020-06-17.test.txt - src_alpha3: tha - tgt_alpha3: eng - short_pair: th-en - chrF2_score: 0.644 - bleu: 48.1 - brevity_penalty: 0.9740000000000001 - ref_len: 7407.0 - src_name: Thai - tgt_name: English - train_date: 2020-06-17 - src_alpha2: th - tgt_alpha2: en - prefer_old: False - long_pair: tha-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 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 enables to specify: - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP) - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html | 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 model on SQuAD. You can run it with BERT Large with the following command: ```bash python run_qa.py \ --model_name_or_path bert-large-uncased-whole-word-masking \ --gaudi_config_name gaudi_config_name_or_path \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 24 \ --per_device_eval_batch_size 8 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/squad/ \ --use_habana \ --use_lazy_mode \ --throughput_warmup_steps 2 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. | 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 | 0.4661 | | 0.3008 | 4.02 | 2000 | 0.4194 | 0.4228 | | 0.2316 | 5.02 | 2500 | 0.3933 | 0.4099 | | 0.1921 | 6.02 | 3000 | 0.4532 | 0.3965 | | 0.1561 | 7.03 | 3500 | 0.4315 | 0.3777 | | 0.1378 | 8.03 | 4000 | 0.4463 | 0.3847 | | 0.1222 | 9.04 | 4500 | 0.4402 | 0.3784 | | 0.1076 | 10.04 | 5000 | 0.4253 | 0.3735 | | 0.0924 | 11.04 | 5500 | 0.4844 | 0.3732 | | 0.0866 | 12.05 | 6000 | 0.4758 | 0.3646 | | 0.086 | 13.05 | 6500 | 0.6395 | 0.4594 | | 0.0763 | 14.06 | 7000 | 0.4951 | 0.3647 | | 0.0684 | 15.06 | 7500 | 0.4870 | 0.3577 | | 0.0616 | 16.06 | 8000 | 0.5442 | 0.3591 | | 0.0594 | 17.07 | 8500 | 0.5305 | 0.3606 | | 0.0613 | 18.07 | 9000 | 0.5434 | 0.3546 | | 0.0473 | 19.08 | 9500 | 0.4818 | 0.3532 | | 0.0463 | 20.08 | 10000 | 0.5086 | 0.3514 | | 0.042 | 21.08 | 10500 | 0.5017 | 0.3484 | | 0.0365 | 22.09 | 11000 | 0.5129 | 0.3536 | | 0.0336 | 23.09 | 11500 | 0.5411 | 0.3433 | | 0.0325 | 24.1 | 12000 | 0.5307 | 0.3424 | | 0.0282 | 25.1 | 12500 | 0.5261 | 0.3404 | | 0.0245 | 26.1 | 13000 | 0.5306 | 0.3388 | | 0.0257 | 27.11 | 13500 | 0.5242 | 0.3369 | | 0.0234 | 28.11 | 14000 | 0.5216 | 0.3359 | | 0.0221 | 29.12 | 14500 | 0.5255 | 0.3330 | | 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 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 an `object`:     | 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](https://object.pouta.csc.fi/OPUS-MT-models/swc-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/swc-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/swc-en/opus-2020-01-16.eval.txt) | 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 | 0.0889 | | 0.4137 | 2.0 | 9466 | 0.3863 | 0.1061 | 0.0946 | 0.0963 | | 0.3914 | 3.0 | 14199 | 0.3806 | 0.1051 | 0.0938 | 0.0955 | | 0.3946 | 4.0 | 18932 | 0.3786 | 0.1084 | 0.097 | 0.0986 | | 0.3857 | 5.0 | 23665 | 0.3784 | 0.1101 | 0.0991 | 0.1007 | | 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.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:     | 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/cloth/) dataset based on [**allenai/scibert_scivocab_uncased**](https://huggingface.co/allenai/scibert_scivocab_uncased) model. For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP). | 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 unmasker. ```python unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10) ``` 3. Use the unmasker to generate the candidate set of distractors. ```python sent = "I feel [MASK] now. [SEP] happy" cs = unmasker(sent) print(cs) ``` | 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® Tesla T4 in Google Colab | 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 | | ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) | | **SciBERT** | [*cdgp-csg-scibert-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) | | **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) | | **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-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 | 0.5634 | | 0.3465 | 4.0 | 20 | 0.3457 | 0.5634 | | 0.3466 | 5.0 | 25 | 0.3455 | 0.5634 | | 0.3466 | 6.0 | 30 | 0.3455 | 0.5634 | | 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 SOTA-comparable on multiple MIR tasks even under probing settings, while keeping fine-tunable on a single 2080Ti. It outperforms Jukebox representation on GTZAN (genre classification) and GiantSteps (key classification) datasets. Larger models trained with more data are on the way. Noted: we also release a pre-trained MIR model [music2vec](https://huggingface.co/m-a-p/music2vec-v1/blob/main/README.md) before, which shares similar model structure but has weaker performance.  | 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}, year={2022} } @article{li2022map, title={MAP-Music2Vec: A Simple and Effective Baseline 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}, journal={arXiv preprint arXiv:2212.02508}, year={2022} } ``` | 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-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.eval.txt) | 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', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'} - tgt_constituents: {'nld'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.test.txt - src_alpha3: zho - tgt_alpha3: nld - short_pair: zh-nl - chrF2_score: 0.525 - bleu: 31.5 - brevity_penalty: 0.9309999999999999 - ref_len: 13575.0 - src_name: Chinese - tgt_name: Dutch - train_date: 2020-06-17 - src_alpha2: zh - tgt_alpha2: nl - prefer_old: False - long_pair: zho-nld - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 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://object.pouta.csc.fi/OPUS-MT-models/fi-lg/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-lg/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-lg/opus-2020-01-24.eval.txt) | 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 Accuracy: 0.8333 - Train Start Logits Accuracy: 0.7955 - Validation Loss: 0.8305 - Validation End Logits Accuracy: 0.7820 - Validation Start Logits Accuracy: 0.7556 - Epoch: 1 | 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_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.9118 | 0.7405 | 0.7093 | 0.8196 | 0.7744 | 0.7556 | 0 | | 0.5941 | 0.8333 | 0.7955 | 0.8305 | 0.7820 | 0.7556 | 1 | | 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 | 27.5733 | | 0.1506 | 0.3 | 2000 | 0.1514 | 26.0331 | | 0.1373 | 0.4 | 2500 | 0.1494 | 24.4177 | | 0.1298 | 0.5 | 3000 | 0.1456 | 25.0563 | | 0.1198 | 1.06 | 3500 | 0.1436 | 24.4177 | | 0.1102 | 0.1 | 4000 | 0.1452 | 24.2675 | | 0.1097 | 0.2 | 4500 | 0.1402 | 24.3050 | | 0.105 | 0.3 | 5000 | 0.1398 | 23.8167 | | 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, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 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://fengshenbang-lm.com/open-api) | 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/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) We propose an new paradigm for zero-shot learners that is input-agnostic, in the sense that it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, sentiment analysis. Our approach converts zero-shot learning into multiple choice tasks, avoiding problems in commonly used large generative models such as FLAN. It not only adds generalization ability to the models, but also reduces the needs of parameters significantly. We demonstrate that this approach leads to state-of-the-art performance on common language benchmarks, and produces satisfactory results on tasks such as natural language inference and text classification. For more details, please refer to our [paper](https://arxiv.org/abs/2210.08590) or [github](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) | 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://arxiv.org/pdf/2107.07498.pdf)-RoBERTa-110M | 65.4 | 35.5 | 49 | 32.8 | 33 | 60.7 | 14.9 | 50 | 55.6 | 44.1 | | [FineTuning](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M | 66.5 | 57 | 516 | 42.1 | 32 | 60.4 | 15 | 60.1 | 50.3 | 48.34 | | [PET](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M | 84 | 59.9 | 56.4 | 50.3 | 38.1 | 58.4 | 40.6 | 61.1 | 58.7 | 56.39 | | [P-tuning](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M | 80.6 | 56.6 | 55.9 | 52.6 | 35.7 | 60.8 | 39.61 | 51.8 | 55.7 | 54.37 | | [EFL](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M | 76.7 | 47.9 | 56.3 | 52.1 | 48.7 | 54.6 | 30.3 | 52.8 | 52.3 | 52.7 | | [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) | 88.64 | 54.08 | 54.32 | 48.6 | 66.55 | 73.76 | 67.71 | 52.54 | 59.92 | 62.86 | | [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) | 89.53 | 57.3 | 54.25 | 50 | 70.59 | 77.49 | 78.09 | 55.73 | 65.16 | 66.46 | | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **89.278** | **60.9** | **57.46** | 52.89 | **76.33** | **80.37** | **90.33** | 61.73 | **79.15** | **72.05** | **Zero-shot** | Model | eprstmt | csldcp | tnews | iflytek | ocnli | bustm | chid | csl | wsc | Avg | |---------------|-----------|-----------|-----------|-----------|-----------|----------|----------|----------|-----------|-----------| | [GPT](https://arxiv.org/pdf/2107.07498.pdf)-110M | 57.5 | 26.2 | 37 | 19 | 34.4 | 50 | 65.6 | 50.1 | 50.3 | 43.4 | | [PET](https://arxiv.org/pdf/2107.07498.pdf)-RoBERTa-110M | 85.2 | 12.6 | 26.1 | 26.6 | 40.3 | 50.6 | 57.6 | 52.2 | 54.7 | 45.1 | | [NSP-BERT](https://arxiv.org/abs/2109.03564)-110M | 86.9 | 47.6 | 51 | 41.6 | 37.4 | 63.4 | 52 | **64.4** | 59.4 | 55.96 | | [ZeroPrompt](https://arxiv.org/abs/2201.06910)-T5-1.5B | - | - | - | 16.14 | 46.16 | - | - | - | 47.98 | - | | [Yuan1.0-13B](https://arxiv.org/abs/2110.04725) | 88.13 | 38.99 | 57.47 | 38.82 | 48.13 | 59.38 | 86.14 | 50 | 38.99 | 56.22 | | [ERNIE3.0-240B](https://arxiv.org/abs/2107.02137) | 88.75 | **50.97** | **57.83** | **40.42** | 53.57 | 64.38 | 87.13 | 56.25 | 53.46 | 61.41 | | [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) | 86.16 | 31.26 | 46.61 | 26.54 | 66.91 | 73.34 | 66.68 | 50.09 | 53.66 | 55.7 | | [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) | 87.5 | 30.4 | 47.6 | 31.5 | 69.9 | 75.9 | 78.17 | 49.5 | 60.55 | 59.01 | | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **88.79** | 42.06 | 55.21 | 33.93 | **75.57** | **79.5** | **89.4** | 50.25 | **66.67** | **64.53** | **Full dataset** | Model | AFQMC | TNEWS1.1 | IFLYTEK | OCNLI | CMNLI | WSC1.1 | CSL | CHID | C3 | |--------------------------------------------|-------|----------|---------|-------|-------|--------|-------|-------|-------| | RoBERTa-Base | 74.06 | 57.5 | 60.36 | 74.3 | 79.73 | 83.48 | 85.37 | - | - | | RoBERTa-Large | 74.88 | 58.79 | 61.52 | 77.7 | 81.4 | 89.14 | 86 | - | - | | [Erlangshen-MegatronBert-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-MegatronBert-1.3B) 「Finetuning」 | 76.08 | 59.38 | 62.34 | 79.14 | 81 | 92.43 | 87.2 | 84.65 | 86.77 | | [Erlangshen-UniMC-MegatronBERT-1.3B-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | 77.09 | 60.4 | 62.67 | 83.05 | 84.76 | 93.74 | 87.67 | 85.93 | 86.54 | | 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_parser) args = total_parser.parse_args() pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese' args.learning_rate=2e-5 args.max_length=512 args.max_epochs=3 args.batchsize=8 args.default_root_dir='./' model = UniMCPipelines(args, pretrained_model_path) train_data = [] dev_data = [] test_data = [ {"texta": "放弃了途观L和荣威RX5，果断入手这部车，外观霸气又好开", "textb": "", "question": "下面新闻属于哪一个类别？", "choice": [ "房产", "汽车", "教育", "科技" ], "answer": "汽车", "label": 1, "id": 7759} ] if args.train: model.train(train_data, dev_data) result = model.predict(test_data) for line in result[:20]: print(line) ``` | 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 | | 1.1855 | 5.0 | 5590 | 1.2101 | | 1.1674 | 6.0 | 6708 | 1.2020 | | 1.1508 | 7.0 | 7826 | 1.1893 | | 1.1386 | 8.0 | 8944 | 1.1870 | | 1.129 | 9.0 | 10062 | 1.1794 | | 1.1193 | 10.0 | 11180 | 1.1759 | | 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 Derrida](https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion) をStable Diffusion 1.4を用い差分マージしました。 そこから[DreamLike Photoreal 1.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0)でIN0~5を調節、さらに30000枚のSD2.1、Novel AI、WD1.3/1.4、CoolJapan Diffusion 2.1、Dreamlike Photoreal 2.0で出力された画像でチューニングされています。 利用の際は以下のPrompt/Negative Promptをおすすめします。 P: best quality, masterpiece NP: 3d, flat shading, flat color, retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb | 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 pink and hetailed yellow eye yellow, water splash in gorgeous scene secret garden Negative prompt: [[[3d]]], (((flat shading, flat color))), retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8, Seed: 1117106368, Size: 512x768, Model hash: 6d1729a039, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires resize: 768x1152, Hires steps: 5, Hires upscaler: Latent ``` <img src="https://huggingface.co/nakayama/DeDeDe/resolve/main/img/image02.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece))), detailed anime style of bunny girl bishoujo from front wearing intricate frill jirai kei bikini with detailed wavy pink hair pink and detailed yellow eye yellow and lying on the bed in (((glitter harajuku kawaii messy room with flower and candy))) Negative prompt: 3d, (((flat shading, flat color))), retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb, eating Steps: 50, Sampler: DPM++ SDE Karras, CFG scale: 5.5, Seed: 911018641, Size: 768x512, Model hash: 6d1729a039, Denoising strength: 0.2, Clip skip: 2, ENSD: 31337, Hires resize: 1152x768, Hires steps: 10, Hires upscaler: ESRGAN_4x ``` <img src="https://huggingface.co/nakayama/DeDeDe/resolve/main/img/image03.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece))), detailed anime style of 1girl bishoujo full body standing and looking from viewer and wearing classical frill dress with cape with wavy detailed pink hair pink and detailed yellow hair yellow in scenic view british fantastic landscape with flowing and golden hour, dynamic pose Negative prompt: [[[[3d]]]], (((flat shading, flat color))), retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb Steps: 15, Sampler: DPM++ SDE Karras, CFG scale: 5.5, Seed: 2149903685, Size: 768x512, Model hash: 6d1729a039, Denoising strength: 0.75, Clip skip: 2, ENSD: 31337, Hires resize: 1152x768, Hires steps: 10, Hires upscaler: Latent ``` <img src="https://huggingface.co/nakayama/DeDeDe/resolve/main/img/image04.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece, 8k))), detailed anime style of 1boy cowboy shot wearing samurai outfit with flat chest and fighting pose, fist, motion blur Negative prompt: 3d, flat shading, flat color, retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb Steps: 20, Sampler: Euler a, CFG scale: 8, Seed: 2136917906, Size: 512x768, Model hash: 6d1729a039, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires resize: 768x1152, Hires steps: 20, Hires upscaler: Latent ``` <img src="https://huggingface.co/nakayama/DeDeDe/resolve/main/img/image05.png" style="max-width:400px;" width="50%"/> ``` (((best quality, masterpiece, 8k))), detailed photorealistic style of 1boy cowboy shot wearing mad max outfit with flat chest and fighting pose, fist, motion blur, abandoned city background Negative prompt: (((flat shading, flat color))), retro style, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, inaccurate limb Steps: 20, Sampler: Euler a, CFG scale: 8, Seed: 3787706619, Size: 768x512, Model hash: 6d1729a039, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, Hires resize: 1152x768, Hires steps: 5, Hires upscaler: Latent ``` | 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 Difference @ 1.0 | DreamLike Diffusion 1.0(0aecbcfa2c) | TrinArt Characters v2 Derrida(42d3f359b0) | Stable Diffusion 1.4(fe4efff1e1) | DDD_pre1(d1ac03017b) | 2. DDD_pre1にDreamlike Photoreal 1.0でIN00~IN05を階層マージで編集する | Model: A | Model: B | Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | DDD_pre1(d1ac03017b) | Dreamlike Photoreal 1.0(f403e4e2a5) | 0.45,0.45,0.4,0.35,0.3,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 | 0 | DDD_pre2(601ec74593) | 3. DDD_pre2に対し、[自前で用意した他Diffusion Modelの出力からなる素材画像](https://huggingface.co/datasets/nakayama/DeDeDeDataset)にて学習させる 用意の際に利用したサービス/モデルは、SD2.1、Novel AI、WD1.3/1.4、CoolJapan Diffusion 2.1、Dreamlike Photoreal 2.0。 総数は30000程、flipしたものと合わせてlearning rateは5e-6、60000Step学習させた。 これにより生成したモデルをDDD_pre3(4709475652)とする。 4. DDD_pre3にDDD_pre2を加重平均でマージする | Interpolation Method | Primary Model | Secondary Model | Merge Name | | --- | --- | --- | --- | | Weighted Sum @ 0.5 | DDD_pre3(4709475652) | DDD_pre2(601ec74593) | DeDeDe(6d1729a039) | | 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なります。 Copyright (c) 2023 Ren Nakayama Released under the MIT license https://huggingface.co/nakayama/DeDeDe/blob/main/MIT-License | 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。** - **あなたは、モデルカードとファイル（実際の推論やファインチューニングを伴わない）を、商用および非商用のウェブサイト／アプリ／その他に自由にホストすることができます。完全なモデル名（Dreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0）を明記し、モデルカードへのリンク（ https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0 / https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/ ）を含めてください。** - **完全に非商用なウェブサイトやアプリなどで、モデルやその派生物を自由にホストすることができます（収益や寄付を一切得ていないことを意味します）。完全なモデル名（Dreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0）を明記し、モデルカード（ https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0 / https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/ ）へのリンクを添付してください。** - **10人以下のチームで、モデルの出力またはモデルの派生物の出力を商業目的で自由に使用することができます。** - あなたは、違法または有害な出力やコンテンツを意図的に作成したり共有したりするために、このモデルを使用することはできません。 - あなたが生成した出力について、作者はいかなる権利も主張しません。あなたはそれらを自由に使用することができ、ライセンスで定められた規定に反してはならないその使用について責任を負います。 - あなたはウェイトを再配布することができます。再配布する場合は、ライセンスにあるものと同じ使用制限を含め、**修正した**CreativeML OpenRAIL-Mのコピーをすべてのユーザーと共有しなければならないことに注意してください（ライセンスを完全にかつ慎重にお読みください） ライセンス全文はこちらでご覧ください：https://huggingface.co/nakayama/DeDeDe/blob/main/License.md | 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 | | 0.5759 | 3.0 | 102 | 0.6505 | -0.0293 | | 0.5443 | 4.0 | 136 | 0.6320 | 0.0549 | | 0.4957 | 5.0 | 170 | 0.6662 | 0.1327 | | 0.4623 | 6.0 | 204 | 0.7247 | 0.0675 | | 0.4249 | 7.0 | 238 | 0.7533 | 0.0972 | | 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 description into one of **6 play store categories**. Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps). | 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 after 4 epochs. The accuracy of the model on the test set was 0.9033. | 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 = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) classifier("Disney+ has something for everyone and every mood, all in one place. With endless entertainment from Disney, Pixar, Marvel, Star Wars, National Geographic and Star, there's always something exciting to watch. Watch the latest releases, Original series and movies, classic films, throwbacks and so much more.") '''Output''' [{'label': 'Entertainment', 'score': 0.9014402031898499}] ``` | 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. 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`:        | 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 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 an `object`:     | 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 | 5808 | 0.0912 | 0.8709 | 0.8282 | 0.8490 | 0.9819 | | 0.0203 | 3.0 | 8712 | 0.0907 | 0.8666 | 0.8511 | 0.8588 | 0.9834 | | 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_scheduler_warmup_ratio: 0.01 - training_steps: 12589 - mixed_precision_training: Native AMP | 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_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 512, 'force_call_on': [12589], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9b71edc6c769705c1ef1955b6f5cfdd5a7d1b802', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'kejian/spectacular-awr'}, 'objective': {'alpha': 0.05, 'beta': 1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'deliberate-awr', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 12589, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649934336, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 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_scheduler_warmup_steps: 100 - training_steps: 5000 | 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 | | 0.4941 | 0.52 | 400 | 0.3281 | 0.3122 | | 0.4816 | 0.65 | 500 | 0.3154 | 0.3102 | | 0.4649 | 0.78 | 600 | 0.3199 | 0.3058 | | 0.461 | 0.91 | 700 | 0.3047 | 0.2974 | | 0.4613 | 1.04 | 800 | 0.3006 | 0.2900 | | 0.4198 | 1.17 | 900 | 0.2951 | 0.2891 | | 0.3864 | 1.3 | 1000 | 0.2989 | 0.2862 | | 0.3963 | 1.43 | 1100 | 0.2932 | 0.2830 | | 0.3953 | 1.56 | 1200 | 0.2936 | 0.2829 | | 0.3962 | 1.69 | 1300 | 0.2952 | 0.2773 | | 0.3811 | 1.82 | 1400 | 0.2915 | 0.2748 | | 0.3736 | 1.95 | 1500 | 0.2839 | 0.2684 | | 0.3507 | 2.08 | 1600 | 0.2914 | 0.2678 | | 0.3277 | 2.21 | 1700 | 0.2895 | 0.2652 | | 0.3344 | 2.34 | 1800 | 0.2843 | 0.2673 | | 0.335 | 2.47 | 1900 | 0.2821 | 0.2635 | | 0.3559 | 2.6 | 2000 | 0.2830 | 0.2599 | | 0.3254 | 2.73 | 2100 | 0.2711 | 0.2577 | | 0.3263 | 2.86 | 2200 | 0.2685 | 0.2546 | | 0.3266 | 2.99 | 2300 | 0.2679 | 0.2521 | | 0.3066 | 3.12 | 2400 | 0.2727 | 0.2526 | | 0.2998 | 3.25 | 2500 | 0.2648 | 0.2537 | | 0.2961 | 3.38 | 2600 | 0.2630 | 0.2519 | | 0.3046 | 3.51 | 2700 | 0.2684 | 0.2506 | | 0.3006 | 3.64 | 2800 | 0.2604 | 0.2492 | | 0.2992 | 3.77 | 2900 | 0.2682 | 0.2508 | | 0.2775 | 3.9 | 3000 | 0.2732 | 0.2440 | | 0.2903 | 4.03 | 3100 | 0.2659 | 0.2427 | | 0.2535 | 4.16 | 3200 | 0.2650 | 0.2433 | | 0.2714 | 4.29 | 3300 | 0.2588 | 0.2394 | | 0.2636 | 4.42 | 3400 | 0.2652 | 0.2434 | | 0.2647 | 4.55 | 3500 | 0.2624 | 0.2371 | | 0.2796 | 4.67 | 3600 | 0.2611 | 0.2373 | | 0.2644 | 4.8 | 3700 | 0.2604 | 0.2341 | | 0.2657 | 4.93 | 3800 | 0.2567 | 0.2331 | | 0.2423 | 5.06 | 3900 | 0.2594 | 0.2322 | | 0.2556 | 5.19 | 4000 | 0.2587 | 0.2323 | | 0.2327 | 5.32 | 4100 | 0.2639 | 0.2299 | | 0.2613 | 5.45 | 4200 | 0.2569 | 0.2310 | | 0.2382 | 5.58 | 4300 | 0.2585 | 0.2298 | | 0.2404 | 5.71 | 4400 | 0.2543 | 0.2287 | | 0.2368 | 5.84 | 4500 | 0.2553 | 0.2286 | | 0.2514 | 5.97 | 4600 | 0.2517 | 0.2279 | | 0.2415 | 6.1 | 4700 | 0.2524 | 0.2270 | | 0.2338 | 6.23 | 4800 | 0.2540 | 0.2265 | | 0.219 | 6.36 | 4900 | 0.2549 | 0.2263 | | 0.2428 | 6.49 | 5000 | 0.2546 | 0.2261 | | 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 | 440 | 0.0732 | 0.8913 | 0.9122 | 0.9016 | 0.9783 | | 0.1878 | 3.0 | 660 | 0.0681 | 0.8984 | 0.9186 | 0.9083 | 0.9797 | | 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_scheduler_warmup_steps: 1000 - num_epochs: 500 - mixed_precision_training: Native AMP | 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 | 20000 | 0.0495 | | 0.0527 | 63.77 | 25000 | 0.0491 | | 0.0517 | 76.53 | 30000 | 0.0487 | | 0.051 | 89.29 | 35000 | 0.0484 | | 0.0505 | 102.04 | 40000 | 0.0482 | | 0.0502 | 114.79 | 45000 | 0.0480 | | 0.0499 | 127.55 | 50000 | 0.0480 | | 0.0497 | 140.31 | 55000 | 0.0479 | | 0.0495 | 153.06 | 60000 | 0.0478 | | 0.0493 | 165.81 | 65000 | 0.0477 | | 0.0491 | 178.57 | 70000 | 0.0477 | | 0.0489 | 191.33 | 75000 | 0.0476 | | 0.0488 | 204.08 | 80000 | 0.0476 | | 0.0486 | 216.83 | 85000 | 0.0476 | | 0.0485 | 229.59 | 90000 | 0.0475 | | 0.0484 | 242.35 | 95000 | 0.0474 | | 0.0483 | 255.1 | 100000 | 0.0473 | | 0.0482 | 267.86 | 105000 | 0.0473 | | 0.0481 | 280.61 | 110000 | 0.0473 | | 0.048 | 293.37 | 115000 | 0.0472 | | 0.0479 | 306.12 | 120000 | 0.0472 | | 0.0478 | 318.88 | 125000 | 0.0472 | | 0.0477 | 331.63 | 130000 | 0.0471 | | 0.0476 | 344.39 | 135000 | 0.0471 | | 0.0475 | 357.14 | 140000 | 0.0471 | | 0.0475 | 369.9 | 145000 | 0.0471 | | 0.0474 | 382.65 | 150000 | 0.0471 | | 0.0473 | 395.41 | 155000 | 0.0470 | | 0.0473 | 408.16 | 160000 | 0.0470 | | 0.0472 | 420.92 | 165000 | 0.0470 | | 0.0472 | 433.67 | 170000 | 0.0470 | | 0.0472 | 446.43 | 175000 | 0.0470 | | 0.0472 | 459.18 | 180000 | 0.0470 | | 0.0471 | 471.94 | 185000 | 0.0470 | | 0.0471 | 484.69 | 190000 | 0.0470 | | 0.0471 | 497.45 | 195000 | 0.0470 | | 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, label2id=label2id) tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config) text = "Прекрасный день." pipe = pipeline('text-classification', model=model, tokenizer=tokenizer) pipe(text) ``` ```json [{'label': 'неопасный текcт', 'score': 0.9249424934387207}] ``` | 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 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`:      | 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 was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is the one with absolute position embeddings: - `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_medium` Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 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> <code class="language-bash"> See ESPNet repo for how to use pre-trained models </pre></li> <li><strong>Config</strong><pre><code>config: conf/train.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_raw_phn_pypinyin_g2p_phone ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 500 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 500 batch_size: 20 valid_batch_size: null batch_bins: 3750000 valid_batch_bins: null train_shape_file: - exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/text_shape.phn - exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/speech_shape valid_shape_file: - exp/tts_stats_raw_phn_pypinyin_g2p_phone/valid/text_shape.phn - exp/tts_stats_raw_phn_pypinyin_g2p_phone/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 240000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_no_dev/text - text - text - - dump/raw/train_no_dev/wav.scp - speech - sound - - dump/xvector/train_no_dev/xvector.scp - spembs - kaldi_ark valid_data_path_and_name_and_type: - - dump/raw/dev/text - text - text - - dump/raw/dev/wav.scp - speech - sound - - dump/xvector/dev/xvector.scp - spembs - kaldi_ark allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-06 weight_decay: 0.0 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - '' - d - sh - j - i4 - zh - l - x - e - b - g - i1 - h - q - m - u4 - t - z - ch - i3 - i2 - f - s - n - r - ian4 - e4 - ong1 - en2 - ai4 - k - ing2 - a1 - iou3 - uo3 - ao4 - u3 - ui4 - p - e2 - an1 - eng2 - c - in1 - ai2 - an4 - ian2 - ing1 - ai3 - ang4 - ao3 - ian1 - uo4 - ian3 - iao4 - ang1 - u2 - ü4 - u1 - a4 - eng1 - ing4 - üan2 - ie4 - en1 - iu4 - uei4 - ou4 - er4 - e1 - ei4 - an3 - ong2 - uo2 - ang3 - ou1 - ou3 - ong4 - eng4 - an2 - iang4 - a3 - iang1 - ia1 - iao1 - uan4 - ia4 - iu3 - ang2 - uo1 - ei3 - e3 - in4 - iang3 - ü1 - uan1 - en3 - iao3 - ie3 - ao1 - ai1 - ü2 - ing3 - er2 - ü3 - uan3 - üe4 - in3 - en - ei2 - üe2 - ie2 - en4 - ua4 - in2 - iu2 - uan2 - a2 - ie1 - ou2 - ui1 - iang2 - ong3 - i - uang3 - eng3 - ün4 - uang4 - uai4 - iong4 - v3 - iou2 - ui2 - un1 - üan4 - uang1 - ei1 - uang2 - o2 - a - ao2 - iao2 - ui3 - un4 - o1 - ua2 - un2 - uen2 - iu1 - v4 - ua1 - uei1 - üan3 - ün1 - üe1 - ün2 - uen4 - uei3 - uei2 - un3 - iou4 - o4 - er3 - uen1 - iong3 - iou1 - ia3 - üan1 - ia2 - iong1 - üe3 - uen3 - ve4 - iong2 - uai2 - uai1 - ua3 - ün3 - er - uai3 - ia - o3 - v2 - o - ueng1 - ei - '2' - ua - io1 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: pypinyin_g2p_phone feats_extract: fbank feats_extract_conf: n_fft: 2048 hop_length: 300 win_length: 1200 fs: 24000 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/feats_stats.npz tts: tacotron2 tts_conf: embed_dim: 512 elayers: 1 eunits: 512 econv_layers: 3 econv_chans: 512 econv_filts: 5 atype: location adim: 512 aconv_chans: 32 aconv_filts: 15 cumulate_att_w: true dlayers: 2 dunits: 1024 prenet_layers: 2 prenet_units: 256 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 output_activation: null use_batch_norm: true use_concate: true use_residual: false spk_embed_dim: 512 spk_embed_integration_type: add use_gst: true gst_heads: 4 gst_tokens: 16 dropout_rate: 0.5 zoneout_rate: 0.1 reduction_factor: 1 use_masking: true bce_pos_weight: 10.0 use_guided_attn_loss: true guided_attn_loss_sigma: 0.4 guided_attn_loss_lambda: 1.0 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: 0.10.2a1 distributed: false</code></pre></li> </ul> | 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 | | 0.2132 | 5.6 | 1500 | 0.8758 | 0.3651 | | 0.1523 | 7.46 | 2000 | 0.9977 | 0.3768 | | 0.1192 | 9.33 | 2500 | 1.0482 | 0.4193 | | 0.0964 | 11.19 | 3000 | 1.2212 | 0.4034 | | 0.0824 | 13.06 | 3500 | 1.4391 | 0.3765 | | 0.0736 | 14.93 | 4000 | 1.5250 | 0.3249 | | 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 sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-ml/opus+bt+bt-2020-04-28.zip) * test set translations: [opus+bt+bt-2020-04-28.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ml/opus+bt+bt-2020-04-28.test.txt) * test set scores: [opus+bt+bt-2020-04-28.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ml/opus+bt+bt-2020-04-28.eval.txt) | 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、一般的なモデルカードの日本語訳です。 | 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) This repo contains models, code and pointers to datasets from our paper: [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations](https://arxiv.org/abs/2209.07562). [[PDF]](https://arxiv.org/pdf/2209.07562.pdf) [[HuggingFace Models]](https://huggingface.co/Twitter) | 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 social engagements within a Twitter Heterogeneous Information Network (TwHIN). TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation** tasks such as predicting user to Tweet engagement. | 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 parameters | [Twitter/TwHIN-BERT-base](https://huggingface.co/Twitter/twhin-bert-base) | | TwHIN-BERT-large | 550M parameters | [Twitter/TwHIN-BERT-large](https://huggingface.co/Twitter/twhin-bert-large) | To use these models in 🤗 Transformers: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-large') model = AutoModel.from_pretrained('Twitter/twhin-bert-large') inputs = tokenizer("I'm using TwHIN-BERT! | 84e3be96f6e8577fc87ab4c5a47a4472 |
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