license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['exbert'] | false | Pretraining The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning rate after. | cfd15da969ea3488c78e45a8c0912bb8 |
mit | ['exbert'] | false | Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 | | 7d19093b068095eb42ff101010f2b796 |
mit | ['exbert'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=roberta-base"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> | c0ec15c46213d3347ad38aecff86ef85 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_120k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 120k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | f221add3e5cff43feed4787b530a34d2 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_120k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_120k') model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_120k') model = BertModel.from_pretrained("google/multiberts-seed_1-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 208b61fbd36b74160ea8b2733c8f7cff |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Naija (pcm) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:53:44.595 | 11eca49c57b89b5ca2df7746fd84429a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 2 | c2d09471d77d0150248fcafca87bc4bb |
apache-2.0 | ['translation'] | false | eng-zle * source group: English * target group: East Slavic languages * OPUS readme: [eng-zle](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zle/README.md) * model: transformer * source language(s): eng * target language(s): bel bel_Latn orv_Cyrl rue rus ukr * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-02.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opus2m-2020-08-02.zip) * test set translations: [opus2m-2020-08-02.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opus2m-2020-08-02.test.txt) * test set scores: [opus2m-2020-08-02.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opus2m-2020-08-02.eval.txt) | a79361e9e213a6d35d0ed13a962844ea |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012-engrus.eng.rus | 27.4 | 0.550 | | newstest2013-engrus.eng.rus | 21.4 | 0.493 | | newstest2015-enru-engrus.eng.rus | 24.2 | 0.534 | | newstest2016-enru-engrus.eng.rus | 23.3 | 0.518 | | newstest2017-enru-engrus.eng.rus | 25.3 | 0.541 | | newstest2018-enru-engrus.eng.rus | 22.4 | 0.527 | | newstest2019-enru-engrus.eng.rus | 24.1 | 0.505 | | Tatoeba-test.eng-bel.eng.bel | 20.8 | 0.471 | | Tatoeba-test.eng.multi | 37.2 | 0.580 | | Tatoeba-test.eng-orv.eng.orv | 0.6 | 0.130 | | Tatoeba-test.eng-rue.eng.rue | 1.4 | 0.168 | | Tatoeba-test.eng-rus.eng.rus | 41.3 | 0.616 | | Tatoeba-test.eng-ukr.eng.ukr | 38.7 | 0.596 | | 373ffe2f24730fd16d209f7274e1f2bd |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-zle - source_languages: eng - target_languages: zle - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zle/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'be', 'ru', 'uk', 'zle'] - src_constituents: {'eng'} - tgt_constituents: {'bel', 'orv_Cyrl', 'bel_Latn', 'rus', 'ukr', 'rue'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opus2m-2020-08-02.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-zle/opus2m-2020-08-02.test.txt - src_alpha3: eng - tgt_alpha3: zle - short_pair: en-zle - chrF2_score: 0.58 - bleu: 37.2 - brevity_penalty: 0.9890000000000001 - ref_len: 63493.0 - src_name: English - tgt_name: East Slavic languages - train_date: 2020-08-02 - src_alpha2: en - tgt_alpha2: zle - prefer_old: False - long_pair: eng-zle - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 5991d631f7b6cdf21c55edf1a02ae4c1 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-en-to-th This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0527 - Bleu: 0.0 - Gen Len: 17.5726 | 61305827b9fb76caf147573517837ab6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:| | 0.0414 | 1.0 | 17810 | 0.0527 | 0.0 | 17.5726 | | e65a677f596e7d28b08aac37e923e0d5 |
cc-by-4.0 | [] | false | roberta-base for QA This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. | 26b0150a34a6903e2dbbc46f239accd8 |
cc-by-4.0 | [] | false | Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` | 5163eb43efbbf31f8f45c75ea9ccaaf7 |
cc-by-4.0 | [] | false | Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "HasAns_total": 5928, "NoAns_exact": 81.79983179142137, "NoAns_f1": 81.79983179142137, "NoAns_total": 5945 ``` | edf34f6a27a549dc0b030b5d6e5cfbaf |
mit | [] | false | GBA FE Class Cards on Stable Diffusion This is the `classcard` 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`:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | ef237e8b99f8c37217e3cc6b1d376ec0 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-travel-4-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1384 - Accuracy: 0.4289 | 1f7c67b8a0dd37cb1db0b2b0c7a6ce34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7625 | 1.0 | 1 | 2.5258 | 0.2933 | | 2.0955 | 2.0 | 2 | 2.3775 | 0.3333 | | 1.7076 | 3.0 | 3 | 2.2590 | 0.38 | | 1.3257 | 4.0 | 4 | 2.1788 | 0.4089 | | 1.1109 | 5.0 | 5 | 2.1384 | 0.4289 | | 949d128bf790295a41fd4c0cc420274f |
apache-2.0 | ['generated_from_keras_callback'] | false | adeebt/opus-mt-en-ml-finetuned-en-to-ml This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ml](https://huggingface.co/Helsinki-NLP/opus-mt-en-ml) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.5102 - Validation Loss: 2.2650 - Train Bleu: 6.9525 - Train Gen Len: 22.3542 - Epoch: 0 | 79e762db93a76bf64f9c57a0415814c2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0002, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | edd8b364c17b2733978363618e28cc0d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch | |:----------:|:---------------:|:----------:|:-------------:|:-----:| | 2.5102 | 2.2650 | 6.9525 | 22.3542 | 0 | | 890742843f6082e06bf1689f35005e22 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-ira-olympus-3500 được train bởi hr16 bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVmt2oX7733W44b0) <br> Hoặc test bằng `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Ảnh mẫu của concept: WIP | 657bcb3fda43f7e22f051d641fa6ff4d |
apache-2.0 | ['generated_from_trainer'] | false | code-vs-nl This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [bookcorpus](https://huggingface.co/datasets/bookcorpus) for text and [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) for code datasets. It achieves the following results on the evaluation set: - Loss: 0.5180 - Accuracy: 0.9951 - F1 Score: 0.9950 | 531bc75527f9112c8e66cfed2a1caeb4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 256 - eval_batch_size: 1024 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 | 62e867de8d69ed5ad81845fa2477e090 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.5732 | 0.07 | 500 | 0.5658 | 0.9934 | 0.9934 | | 0.5254 | 0.14 | 1000 | 0.5180 | 0.9951 | 0.9950 | | eaef2603d4f0b24c64d5e7da6b73a008 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_xvector_transformer` ♻️ Imported from https://zenodo.org/record/4393279/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 4cd57968b5a11b05e19700583ce5ebbf |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-230103-091356 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4360 - Mae: 0.4251 - Rmse: 0.6169 - Abs Rel: 0.4500 - Log Mae: 0.1721 - Log Rmse: 0.2269 - Delta1: 0.3828 - Delta2: 0.6326 - Delta3: 0.8051 | 4721a3611374835354d8bfca2321fd52 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 100 - mixed_precision_training: Native AMP | 9b420df4728d7d3a6608910a587a2172 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0762 | 1.0 | 72 | 0.5031 | 0.4779 | 0.6690 | 0.5503 | 0.2006 | 0.2591 | 0.3020 | 0.5337 | 0.8000 | | 0.478 | 2.0 | 144 | 0.4653 | 0.4509 | 0.6307 | 0.4891 | 0.1861 | 0.2377 | 0.3300 | 0.5805 | 0.7734 | | 0.4668 | 3.0 | 216 | 0.4845 | 0.4712 | 0.6373 | 0.5469 | 0.1963 | 0.2471 | 0.3110 | 0.5254 | 0.7235 | | 0.4389 | 4.0 | 288 | 0.4587 | 0.4368 | 0.6219 | 0.4887 | 0.1787 | 0.2344 | 0.3578 | 0.6099 | 0.7926 | | 0.4626 | 5.0 | 360 | 0.4879 | 0.4662 | 0.6351 | 0.5617 | 0.1937 | 0.2482 | 0.3135 | 0.5462 | 0.7395 | | 0.4534 | 6.0 | 432 | 0.4638 | 0.4422 | 0.6236 | 0.4951 | 0.1810 | 0.2358 | 0.3606 | 0.5844 | 0.7831 | | 0.4108 | 7.0 | 504 | 0.4688 | 0.4508 | 0.6279 | 0.5050 | 0.1856 | 0.2385 | 0.3426 | 0.5701 | 0.7623 | | 0.3832 | 8.0 | 576 | 0.4759 | 0.4533 | 0.6284 | 0.5257 | 0.1869 | 0.2411 | 0.3331 | 0.5701 | 0.7617 | | 0.4097 | 9.0 | 648 | 0.4771 | 0.4501 | 0.6303 | 0.5361 | 0.1855 | 0.2433 | 0.3454 | 0.5838 | 0.7609 | | 0.3799 | 10.0 | 720 | 0.4575 | 0.4375 | 0.6240 | 0.4874 | 0.1790 | 0.2349 | 0.3669 | 0.6032 | 0.7916 | | 0.3659 | 11.0 | 792 | 0.4718 | 0.4590 | 0.6298 | 0.5176 | 0.1893 | 0.2396 | 0.3283 | 0.5502 | 0.7368 | | 0.4145 | 12.0 | 864 | 0.4776 | 0.4561 | 0.6298 | 0.5325 | 0.1883 | 0.2421 | 0.3333 | 0.5611 | 0.7540 | | 0.4224 | 13.0 | 936 | 0.4320 | 0.4138 | 0.6202 | 0.4013 | 0.1655 | 0.2232 | 0.4217 | 0.6641 | 0.8004 | | 0.4142 | 14.0 | 1008 | 0.4597 | 0.4440 | 0.6234 | 0.4842 | 0.1813 | 0.2330 | 0.3520 | 0.5895 | 0.7617 | | 0.4393 | 15.0 | 1080 | 0.4333 | 0.4251 | 0.6197 | 0.4182 | 0.1712 | 0.2225 | 0.3787 | 0.6303 | 0.8100 | | 0.4045 | 16.0 | 1152 | 0.4603 | 0.4356 | 0.6197 | 0.4819 | 0.1776 | 0.2322 | 0.3635 | 0.6050 | 0.7858 | | 0.3708 | 17.0 | 1224 | 0.4738 | 0.4567 | 0.6292 | 0.5264 | 0.1886 | 0.2411 | 0.3283 | 0.5557 | 0.7596 | | 0.4042 | 18.0 | 1296 | 0.5004 | 0.4802 | 0.6423 | 0.6101 | 0.2008 | 0.2560 | 0.3022 | 0.5165 | 0.6931 | | 0.3763 | 19.0 | 1368 | 0.4501 | 0.4361 | 0.6213 | 0.4723 | 0.1772 | 0.2303 | 0.3634 | 0.6034 | 0.7889 | | 0.4084 | 20.0 | 1440 | 0.4272 | 0.4133 | 0.6208 | 0.3958 | 0.1649 | 0.2226 | 0.4284 | 0.6684 | 0.8009 | | 0.3637 | 21.0 | 1512 | 0.4307 | 0.4145 | 0.6199 | 0.4134 | 0.1665 | 0.2241 | 0.3957 | 0.6847 | 0.8137 | | 0.3655 | 22.0 | 1584 | 0.4591 | 0.4374 | 0.6370 | 0.4594 | 0.1791 | 0.2384 | 0.3816 | 0.6264 | 0.7826 | | 0.3844 | 23.0 | 1656 | 0.4692 | 0.4444 | 0.6273 | 0.5241 | 0.1824 | 0.2407 | 0.3540 | 0.5990 | 0.7756 | | 0.428 | 24.0 | 1728 | 0.4982 | 0.4753 | 0.6403 | 0.6084 | 0.1984 | 0.2552 | 0.3099 | 0.5233 | 0.7204 | | 0.4051 | 25.0 | 1800 | 0.4824 | 0.4618 | 0.6329 | 0.5533 | 0.1915 | 0.2461 | 0.3248 | 0.5495 | 0.7415 | | 0.3584 | 26.0 | 1872 | 0.4434 | 0.4207 | 0.6177 | 0.4468 | 0.1694 | 0.2277 | 0.3975 | 0.6442 | 0.8038 | | 0.3443 | 27.0 | 1944 | 0.4602 | 0.4434 | 0.6241 | 0.4912 | 0.1822 | 0.2351 | 0.3431 | 0.5877 | 0.7893 | | 0.3714 | 28.0 | 2016 | 0.4818 | 0.4594 | 0.6316 | 0.5521 | 0.1900 | 0.2455 | 0.3283 | 0.5567 | 0.7493 | | 0.3688 | 29.0 | 2088 | 0.4443 | 0.4215 | 0.6242 | 0.4386 | 0.1702 | 0.2294 | 0.4024 | 0.6522 | 0.8065 | | 0.3615 | 30.0 | 2160 | 0.4462 | 0.4291 | 0.6189 | 0.4500 | 0.1739 | 0.2277 | 0.3792 | 0.6208 | 0.7896 | | 0.3655 | 31.0 | 2232 | 0.4808 | 0.4574 | 0.6305 | 0.5524 | 0.1893 | 0.2452 | 0.3322 | 0.5590 | 0.7460 | | 0.3576 | 32.0 | 2304 | 0.4321 | 0.4102 | 0.6182 | 0.4079 | 0.1640 | 0.2241 | 0.4296 | 0.6713 | 0.8074 | | 0.3947 | 33.0 | 2376 | 0.4468 | 0.4298 | 0.6232 | 0.4574 | 0.1744 | 0.2306 | 0.3873 | 0.6163 | 0.7873 | | 0.3402 | 34.0 | 2448 | 0.4565 | 0.4352 | 0.6195 | 0.4913 | 0.1776 | 0.2337 | 0.3734 | 0.6039 | 0.7865 | | 0.3412 | 35.0 | 2520 | 0.4438 | 0.4261 | 0.6180 | 0.4546 | 0.1728 | 0.2279 | 0.3778 | 0.6252 | 0.8043 | | 0.3547 | 36.0 | 2592 | 0.4577 | 0.4416 | 0.6218 | 0.4868 | 0.1807 | 0.2329 | 0.3517 | 0.5862 | 0.7862 | | 0.3425 | 37.0 | 2664 | 0.4682 | 0.4511 | 0.6285 | 0.5210 | 0.1860 | 0.2406 | 0.3411 | 0.5748 | 0.7694 | | 0.3853 | 38.0 | 2736 | 0.4752 | 0.4514 | 0.6289 | 0.5458 | 0.1863 | 0.2438 | 0.3408 | 0.5721 | 0.7760 | | 0.3643 | 39.0 | 2808 | 0.4737 | 0.4547 | 0.6291 | 0.5401 | 0.1875 | 0.2428 | 0.3316 | 0.5673 | 0.7617 | | 0.398 | 40.0 | 2880 | 0.4662 | 0.4467 | 0.6274 | 0.5124 | 0.1838 | 0.2394 | 0.3514 | 0.5823 | 0.7700 | | 0.3579 | 41.0 | 2952 | 0.4781 | 0.4545 | 0.6290 | 0.5513 | 0.1880 | 0.2446 | 0.3343 | 0.5624 | 0.7718 | | 0.3545 | 42.0 | 3024 | 0.4460 | 0.4277 | 0.6221 | 0.4553 | 0.1730 | 0.2294 | 0.3862 | 0.6285 | 0.7999 | | 0.3527 | 43.0 | 3096 | 0.4330 | 0.4153 | 0.6169 | 0.4221 | 0.1668 | 0.2240 | 0.4106 | 0.6618 | 0.8084 | | 0.3251 | 44.0 | 3168 | 0.4503 | 0.4286 | 0.6172 | 0.4781 | 0.1744 | 0.2313 | 0.3725 | 0.6224 | 0.8095 | | 0.3433 | 45.0 | 3240 | 0.4471 | 0.4346 | 0.6187 | 0.4652 | 0.1772 | 0.2293 | 0.3606 | 0.6043 | 0.7952 | | 0.3607 | 46.0 | 3312 | 0.4474 | 0.4263 | 0.6166 | 0.4658 | 0.1728 | 0.2293 | 0.3835 | 0.6287 | 0.8039 | | 0.3722 | 47.0 | 3384 | 0.4527 | 0.4337 | 0.6205 | 0.4857 | 0.1768 | 0.2329 | 0.3696 | 0.6084 | 0.7922 | | 0.3322 | 48.0 | 3456 | 0.4629 | 0.4431 | 0.6236 | 0.5118 | 0.1818 | 0.2373 | 0.3460 | 0.5897 | 0.7954 | | 0.3624 | 49.0 | 3528 | 0.4431 | 0.4304 | 0.6203 | 0.4511 | 0.1742 | 0.2277 | 0.3827 | 0.6152 | 0.7917 | | 0.3386 | 50.0 | 3600 | 0.4475 | 0.4260 | 0.6173 | 0.4697 | 0.1727 | 0.2301 | 0.3870 | 0.6283 | 0.8102 | | 0.3316 | 51.0 | 3672 | 0.4558 | 0.4328 | 0.6194 | 0.4982 | 0.1770 | 0.2345 | 0.3618 | 0.6120 | 0.8124 | | 0.3259 | 52.0 | 3744 | 0.4316 | 0.4084 | 0.6165 | 0.4234 | 0.1630 | 0.2245 | 0.4311 | 0.6809 | 0.8148 | | 0.3299 | 53.0 | 3816 | 0.4489 | 0.4222 | 0.6198 | 0.4779 | 0.1706 | 0.2327 | 0.4049 | 0.6441 | 0.8021 | | 0.3334 | 54.0 | 3888 | 0.4831 | 0.4598 | 0.6319 | 0.5716 | 0.1902 | 0.2476 | 0.3281 | 0.5597 | 0.7549 | | 0.3342 | 55.0 | 3960 | 0.4478 | 0.4288 | 0.6166 | 0.4786 | 0.1745 | 0.2310 | 0.3749 | 0.6218 | 0.8091 | | 0.3276 | 56.0 | 4032 | 0.4524 | 0.4342 | 0.6192 | 0.4852 | 0.1773 | 0.2326 | 0.3596 | 0.6113 | 0.8007 | | 0.326 | 57.0 | 4104 | 0.4411 | 0.4226 | 0.6162 | 0.4486 | 0.1704 | 0.2268 | 0.3947 | 0.6403 | 0.7959 | | 0.3429 | 58.0 | 4176 | 0.4578 | 0.4418 | 0.6221 | 0.4961 | 0.1812 | 0.2349 | 0.3497 | 0.5956 | 0.7750 | | 0.3347 | 59.0 | 4248 | 0.4586 | 0.4409 | 0.6220 | 0.4946 | 0.1808 | 0.2347 | 0.3439 | 0.6004 | 0.7869 | | 0.3215 | 60.0 | 4320 | 0.4583 | 0.4382 | 0.6232 | 0.4974 | 0.1789 | 0.2357 | 0.3667 | 0.6008 | 0.7855 | | 0.331 | 61.0 | 4392 | 0.4412 | 0.4206 | 0.6145 | 0.4579 | 0.1699 | 0.2276 | 0.3966 | 0.6413 | 0.8047 | | 0.3124 | 62.0 | 4464 | 0.4455 | 0.4236 | 0.6181 | 0.4727 | 0.1715 | 0.2313 | 0.3902 | 0.6417 | 0.8098 | | 0.322 | 63.0 | 4536 | 0.4406 | 0.4230 | 0.6143 | 0.4548 | 0.1716 | 0.2269 | 0.3775 | 0.6425 | 0.8115 | | 0.3194 | 64.0 | 4608 | 0.4473 | 0.4331 | 0.6193 | 0.4657 | 0.1765 | 0.2297 | 0.3606 | 0.6122 | 0.8014 | | 0.3159 | 65.0 | 4680 | 0.4407 | 0.4225 | 0.6186 | 0.4548 | 0.1712 | 0.2293 | 0.3913 | 0.6433 | 0.8075 | | 0.3118 | 66.0 | 4752 | 0.4478 | 0.4258 | 0.6169 | 0.4801 | 0.1728 | 0.2315 | 0.3762 | 0.6391 | 0.8064 | | 0.336 | 67.0 | 4824 | 0.4659 | 0.4463 | 0.6252 | 0.5210 | 0.1834 | 0.2394 | 0.3464 | 0.5820 | 0.7786 | | 0.3233 | 68.0 | 4896 | 0.4370 | 0.4208 | 0.6168 | 0.4452 | 0.1696 | 0.2265 | 0.4019 | 0.6425 | 0.8059 | | 0.3285 | 69.0 | 4968 | 0.4479 | 0.4340 | 0.6189 | 0.4773 | 0.1771 | 0.2312 | 0.3609 | 0.6136 | 0.7972 | | 0.3186 | 70.0 | 5040 | 0.4469 | 0.4308 | 0.6198 | 0.4698 | 0.1751 | 0.2310 | 0.3741 | 0.6219 | 0.7966 | | 0.3351 | 71.0 | 5112 | 0.4476 | 0.4292 | 0.6176 | 0.4769 | 0.1745 | 0.2311 | 0.3718 | 0.6220 | 0.8035 | | 0.3286 | 72.0 | 5184 | 0.4415 | 0.4229 | 0.6155 | 0.4655 | 0.1713 | 0.2289 | 0.3816 | 0.6376 | 0.8117 | | 0.3135 | 73.0 | 5256 | 0.4527 | 0.4335 | 0.6198 | 0.4918 | 0.1769 | 0.2338 | 0.3621 | 0.6152 | 0.8036 | | 0.3244 | 74.0 | 5328 | 0.4449 | 0.4290 | 0.6171 | 0.4685 | 0.1746 | 0.2296 | 0.3667 | 0.6234 | 0.8073 | | 0.3253 | 75.0 | 5400 | 0.4450 | 0.4303 | 0.6182 | 0.4680 | 0.1750 | 0.2296 | 0.3703 | 0.6185 | 0.8013 | | 0.3072 | 76.0 | 5472 | 0.4312 | 0.4212 | 0.6161 | 0.4337 | 0.1700 | 0.2242 | 0.3840 | 0.6411 | 0.8104 | | 0.3159 | 77.0 | 5544 | 0.4434 | 0.4314 | 0.6186 | 0.4636 | 0.1754 | 0.2290 | 0.3643 | 0.6171 | 0.7996 | | 0.3176 | 78.0 | 5616 | 0.4319 | 0.4207 | 0.6177 | 0.4330 | 0.1695 | 0.2249 | 0.3889 | 0.6524 | 0.8080 | | 0.3243 | 79.0 | 5688 | 0.4432 | 0.4304 | 0.6186 | 0.4698 | 0.1752 | 0.2302 | 0.3667 | 0.6218 | 0.8058 | | 0.3183 | 80.0 | 5760 | 0.4438 | 0.4288 | 0.6175 | 0.4665 | 0.1742 | 0.2294 | 0.3730 | 0.6235 | 0.8030 | | 0.323 | 81.0 | 5832 | 0.4365 | 0.4248 | 0.6170 | 0.4480 | 0.1716 | 0.2263 | 0.3820 | 0.6313 | 0.8056 | | 0.3348 | 82.0 | 5904 | 0.4385 | 0.4280 | 0.6179 | 0.4532 | 0.1738 | 0.2273 | 0.3651 | 0.6249 | 0.8099 | | 0.2948 | 83.0 | 5976 | 0.4456 | 0.4330 | 0.6190 | 0.4727 | 0.1763 | 0.2305 | 0.3622 | 0.6121 | 0.7981 | | 0.3156 | 84.0 | 6048 | 0.4349 | 0.4236 | 0.6155 | 0.4442 | 0.1712 | 0.2252 | 0.3834 | 0.6331 | 0.8086 | | 0.3227 | 85.0 | 6120 | 0.4352 | 0.4251 | 0.6160 | 0.4423 | 0.1719 | 0.2250 | 0.3799 | 0.6293 | 0.8055 | | 0.3044 | 86.0 | 6192 | 0.4349 | 0.4235 | 0.6165 | 0.4444 | 0.1714 | 0.2259 | 0.3858 | 0.6312 | 0.8108 | | 0.3067 | 87.0 | 6264 | 0.4293 | 0.4214 | 0.6150 | 0.4293 | 0.1700 | 0.2229 | 0.3862 | 0.6397 | 0.8102 | | 0.3083 | 88.0 | 6336 | 0.4260 | 0.4164 | 0.6139 | 0.4229 | 0.1673 | 0.2221 | 0.3989 | 0.6536 | 0.8126 | | 0.2989 | 89.0 | 6408 | 0.4381 | 0.4270 | 0.6168 | 0.4526 | 0.1731 | 0.2270 | 0.3766 | 0.6248 | 0.8051 | | 0.3232 | 90.0 | 6480 | 0.4352 | 0.4230 | 0.6158 | 0.4480 | 0.1711 | 0.2263 | 0.3854 | 0.6358 | 0.8112 | | 0.3201 | 91.0 | 6552 | 0.4361 | 0.4242 | 0.6164 | 0.4462 | 0.1718 | 0.2262 | 0.3842 | 0.6327 | 0.8078 | | 0.3096 | 92.0 | 6624 | 0.4390 | 0.4273 | 0.6171 | 0.4563 | 0.1733 | 0.2279 | 0.3790 | 0.6237 | 0.8046 | | 0.322 | 93.0 | 6696 | 0.4338 | 0.4229 | 0.6157 | 0.4447 | 0.1709 | 0.2258 | 0.3889 | 0.6351 | 0.8069 | | 0.3096 | 94.0 | 6768 | 0.4348 | 0.4238 | 0.6160 | 0.4448 | 0.1714 | 0.2256 | 0.3839 | 0.6342 | 0.8077 | | 0.3067 | 95.0 | 6840 | 0.4414 | 0.4298 | 0.6181 | 0.4628 | 0.1748 | 0.2290 | 0.3707 | 0.6205 | 0.8027 | | 0.3198 | 96.0 | 6912 | 0.4334 | 0.4228 | 0.6162 | 0.4434 | 0.1709 | 0.2258 | 0.3872 | 0.6370 | 0.8077 | | 0.295 | 97.0 | 6984 | 0.4367 | 0.4261 | 0.6169 | 0.4507 | 0.1728 | 0.2269 | 0.3791 | 0.6283 | 0.8045 | | 0.305 | 98.0 | 7056 | 0.4373 | 0.4266 | 0.6171 | 0.4524 | 0.1730 | 0.2273 | 0.3781 | 0.6280 | 0.8046 | | 0.3304 | 99.0 | 7128 | 0.4334 | 0.4230 | 0.6162 | 0.4432 | 0.1709 | 0.2257 | 0.3874 | 0.6378 | 0.8062 | | 0.3099 | 100.0 | 7200 | 0.4360 | 0.4251 | 0.6169 | 0.4500 | 0.1721 | 0.2269 | 0.3828 | 0.6326 | 0.8051 | | e921bbfeb39847ebb734416182df0e6a |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-finetuned-dogfood This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the lewtun/dog_food dataset. It achieves the following results on the evaluation set: - Loss: 0.9277 - Accuracy: 0.7253 | 1ace32080d355273b1ecfae151fa6ef8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - 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: 0.1 - num_epochs: 1 | 3b5859c9570aac5592202b637ad85a4f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0681 | 1.0 | 16 | 0.9125 | 0.7422 | | ba602832df205bded785d99df4488bc9 |
apache-2.0 | ['whisper-event'] | false | Whisper Kannada Small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Kannada data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | a39ccfadd8336c9fb0ef0bd42836ddf9 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - training_steps: 12033 (terminated upon convergence. Initially set to 51570 steps) - mixed_precision_training: True | 63c0af988a387e55dd5c372c6a36b0d9 |
mit | ['generated_from_keras_callback'] | false | DLL888/roberta-base-squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7054 - Train End Logits Accuracy: 0.8022 - Train Start Logits Accuracy: 0.7586 - Validation Loss: 0.8224 - Validation End Logits Accuracy: 0.7692 - Validation Start Logits Accuracy: 0.7402 - Epoch: 1 | f293fd76bbbb000e5c6ce7b40292e4dc |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10570, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 500, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: mixed_float16 | ca2c663af14132432b23983fd664c494 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.1613 | 0.7038 | 0.6632 | 0.8676 | 0.7626 | 0.7342 | 0 | | 0.7054 | 0.8022 | 0.7586 | 0.8224 | 0.7692 | 0.7402 | 1 | | 954974e5d939dce644ff9db184565b62 |
apache-2.0 | ['question_generator', 'generated_from_trainer'] | false | t5-base-squadqtngen This model is a fine-tuned version of [ManujArora/t5-base-squadqtngen](https://huggingface.co/ManujArora/t5-base-squadqtngen) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7049 | 8e2b137b16370e836aca6dfe6593823a |
apache-2.0 | ['question_generator', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 248 | 1.6398 | | No log | 2.0 | 496 | 1.6440 | | No log | 3.0 | 744 | 1.6594 | | No log | 4.0 | 992 | 1.6720 | | No log | 5.0 | 1240 | 1.6824 | | No log | 6.0 | 1488 | 1.6949 | | No log | 7.0 | 1736 | 1.7032 | | No log | 8.0 | 1984 | 1.7049 | | 1dc076c8d34fb123a62673bd12f4cef3 |
apache-2.0 | [] | false | 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk This model has been trained on noisy data in order to make the acoustic model robust to noisy audio data. This model has apostrophes and hyphens. The language model is trained on the texts of the Common Voice dataset, which is used during training. Special thanks for noised data to **Dmytro Chaplynsky**, https://lang.org.ua Noisy dataset: - Transcriptions: https://www.dropbox.com/s/ohj3y2cq8f4207a/transcriptions.zip?dl=0 - Audio files: https://www.dropbox.com/s/v8crgclt9opbrv1/data.zip?dl=0 Metrics: | Dataset | CER | WER | |-|-|-| | CV10 (no LM) | 0.0515 | 0.2617 | | CV10 (with LM) | 0.0148 | 0.0524 | Metrics on noisy data with [standard model](https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm): | Dataset | CER | WER | |-|-|-| | CV10 (no LM) | 0.1064 | 0.3926 | | CV10 (with LM) | 0.0497 | 0.1265 | More: - The same model, but trained on raw Common Voice data: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm | 8be2d09d808064d880e2be8702e02b21 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner1 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: 0.0584 - Precision: 0.9286 - Recall: 0.9475 - F1: 0.9379 - Accuracy: 0.9859 | 1b6ee7e23dc4c0e6ba0714720649aa26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2183 | 1.0 | 878 | 0.0753 | 0.9087 | 0.9291 | 0.9188 | 0.9800 | | 0.0462 | 2.0 | 1756 | 0.0614 | 0.9329 | 0.9470 | 0.9399 | 0.9858 | | 0.0244 | 3.0 | 2634 | 0.0584 | 0.9286 | 0.9475 | 0.9379 | 0.9859 | | b4a0de6b1b7f50eb840fcacaea3fe7c6 |
mit | [] | false | Bamse og kylling on Stable Diffusion This is the `<bamse-kylling>` 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`:      | 3e567e393b24201e0af42f443d104a4a |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: no | 76f4d4ad311ef4fe3ea5a9df8f6690fb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_7_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0462 - F1: 0.7836 | b183fef04ee40b3942f0d3f908471f61 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.5719 | 0.7490 | | 0.5541 | 2.0 | 582 | 0.5563 | 0.7836 | | 0.5541 | 3.0 | 873 | 0.7301 | 0.7849 | | 0.2509 | 4.0 | 1164 | 0.8073 | 0.7926 | | 0.2509 | 5.0 | 1455 | 1.0842 | 0.7823 | | 0.1182 | 6.0 | 1746 | 1.1721 | 0.7900 | | 0.0537 | 7.0 | 2037 | 1.4060 | 0.7785 | | 0.0537 | 8.0 | 2328 | 1.4497 | 0.7836 | | 0.0262 | 9.0 | 2619 | 1.4722 | 0.7708 | | 0.0262 | 10.0 | 2910 | 1.6529 | 0.7772 | | 0.0131 | 11.0 | 3201 | 1.6573 | 0.7862 | | 0.0131 | 12.0 | 3492 | 1.6986 | 0.7823 | | 0.0115 | 13.0 | 3783 | 1.7765 | 0.7810 | | 0.0098 | 14.0 | 4074 | 1.8036 | 0.7862 | | 0.0098 | 15.0 | 4365 | 1.7684 | 0.7926 | | 0.0028 | 16.0 | 4656 | 1.8385 | 0.7836 | | 0.0028 | 17.0 | 4947 | 1.7903 | 0.7887 | | 0.0054 | 18.0 | 5238 | 1.9065 | 0.7810 | | 0.0007 | 19.0 | 5529 | 1.9331 | 0.7875 | | 0.0007 | 20.0 | 5820 | 1.9384 | 0.7849 | | 0.0006 | 21.0 | 6111 | 1.8687 | 0.7887 | | 0.0006 | 22.0 | 6402 | 2.0603 | 0.7785 | | 0.0009 | 23.0 | 6693 | 2.0403 | 0.7836 | | 0.0009 | 24.0 | 6984 | 2.0348 | 0.7810 | | 0.0005 | 25.0 | 7275 | 2.0462 | 0.7836 | | 19527ac72cb489485c27e460f1b0be0d |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0979 - Accuracy: 0.9682 - F1: 0.8332 - Recall: 0.8466 - Precision: 0.8202 | 93c77d96fa68c6c3d986c60965c708ca |
mit | ['text-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP | cb5c4cb7be59353674eaae820865c364 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.1539 | 1.0 | 6667 | 0.1237 | 0.9584 | 0.7668 | 0.7307 | 0.8067 | | 0.1271 | 2.0 | 13334 | 0.0979 | 0.9682 | 0.8332 | 0.8466 | 0.8202 | | b1663ba3a852e61ddbdeeb5a1e997cca |
['mit'] | [] | false | gunghio/xlm-roberta-base-finetuned-panx-ner This model was trained starting from xlm-roberta-base on a subset of xtreme dataset. `xtreme` datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish. Only 75% of the whole dataset was used. | 37786783ecaf13ef115f1374f82e594e |
['mit'] | [] | false | Training results It achieves the following results on the evaluation set: - Precision: 0.8744154472771157 - Recall: 0.8791424269015351 - F1: 0.8767725659462058 - Accuracy: 0.9432040948504613 Details: | Label | Precision | Recall | F1-Score | Support | |---------|-----------|--------|----------|---------| | PER | 0.922 | 0.908 | 0.915 | 26639 | | LOC | 0.880 | 0.906 | 0.892 | 37623 | | ORG | 0.821 | 0.816 | 0.818 | 28045 | | Overall | 0.874 | 0.879 | 0.877 | 92307 | | b74057e75e51afe86a85232e2b5ac81e |
['mit'] | [] | false | transformers.TokenClassificationPipeline). ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner") model = AutoModelForTokenClassification.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first") example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` | 8b4b4397b1e0d363c921d7ef3966022f |
apache-2.0 | ['pytorch', 'diffusers'] | false | Abstract An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models that offer detailed control of only specific instruments, or raw waveform models that can train on any music but with minimal control and slow generation. In this work, we focus on a middle ground of neural synthesizers that can generate audio from MIDI sequences with arbitrary combinations of instruments in realtime. This enables training on a wide range of transcription datasets with a single model, which in turn offers note-level control of composition and instrumentation across a wide range of instruments. We use a simple two-stage process: MIDI to spectrograms with an encoder-decoder Transformer, then spectrograms to audio with a generative adversarial network (GAN) spectrogram inverter. We compare training the decoder as an autoregressive model and as a Denoising Diffusion Probabilistic Model (DDPM) and find that the DDPM approach is superior both qualitatively and as measured by audio reconstruction and Fréchet distance metrics. Given the interactivity and generality of this approach, we find this to be a promising first step towards interactive and expressive neural synthesis for arbitrary combinations of instruments and notes. <img src="https://storage.googleapis.com/music-synthesis-with-spectrogram-diffusion/architecture.png" alt="Architecture diagram"> | 8386957d5394f7a130eb8e19e53083a5 |
apache-2.0 | [] | false | Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) - **Languages:** Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:** <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> | 83535acfca033749c998e79cce3ba9c7 |
apache-2.0 | [] | false | pip install -q transformers from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-p3" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> | af83ea72b63dfd4073ecd097dbddc406 |
apache-2.0 | [] | false | pip install -q transformers accelerate from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-p3" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> | 467f020730c6b0904a362ed3e735e31f |
apache-2.0 | [] | false | pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-p3" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> <!-- Necessary for whitespace --> | 8685ca454d0637444486f11315028333 |
apache-2.0 | [] | false | Model - **Architecture:** Same as [mt5-xxl](https://huggingface.co/google/mt5-xxl), also refer to the `config.json` file - **Finetuning steps:** 7000 - **Finetuning tokens:** 1.29 billion - **Precision:** bfloat16 | 7b0b729046da8b1b437e787e96958652 |
mit | ['fill-mask', 'generated_from_trainer'] | false | deberta-v3-large-dapt-scientific-papers-pubmed-tapt This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4429 - Accuracy: 0.5915 | bedb6a4cd759516523aca192d6f1e1d5 |
mit | ['fill-mask', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | bca91d46b19b772b788de99531e202dc |
mit | ['fill-mask', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.3855 | 1.0 | 4134 | 3.2334 | 0.4953 | | 2.9224 | 2.0 | 8268 | 2.8317 | 0.5430 | | 2.703 | 3.0 | 12402 | 2.6141 | 0.5665 | | 2.4963 | 4.0 | 16536 | 2.4918 | 0.5855 | | 2.399 | 5.0 | 20670 | 2.4429 | 0.5915 | | f046abc610b51046ba6a699b303574e4 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-fr_s237 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](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. | 5b5b4d97cc5bdeeff0b7d7c89b1bf805 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3926 - F1: 0.6991 | bd1141459ad1d17b4984db976e70b52d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1415 | 1.0 | 50 | 0.5404 | 0.5163 | | 0.5045 | 2.0 | 100 | 0.4347 | 0.6498 | | 0.371 | 3.0 | 150 | 0.3926 | 0.6991 | | 4e1f5876a062c7c6727b311209edf056 |
other | [] | false | JurisBert JurisBert, es una iniciativa de la **Suprema Corte de Justicia de la Nación (SCJN) de México**, nace en agosto del 2020, a propuesta de la **Unidad General de Administración del Conocimiento Jurídico (UGACJ)**, para entrenar un Modelo del Lenguaje contextualizado al ámbito jurídico. Su principal objetivo es generar aplicaciones de **Procesamiento del Lenguaje Natural (PLN)** que coadyuven a la labor jurisdiccional del Alto Tribunal mediante el aprovechamiento del conocimiento de la SCJN plasmado en documentos no estructurados que generan las áreas jurisdiccionales. En 2021, esta iniciativa tomó mayor relevancia con la llegada de la Reforma Judicial y el inicio de la undécima época del SJF, puesto que la creación de JurisBert tiene como objetivos principales la ayuda a la identificación del precedente y la creación de Plataformas de Recuperación de Información. Como parte de la Transformación Digital impulsada por la SCJN, en razón de generar un esquema de “Gobierno Abierto” mediante la Colaboración e Innovación y en el contexto de la operación remota obligada por la contingencia sanitaria derivada del virus SARS COV 2, se pone a disposición de toda la comunidad esta innovación tecnológica pretendiendo con ello la retribución del conocimiento generado por el Alto Tribunal a la ciudadanía. Es su primer versión, JurisBert es un modelo del lenguaje basado en Transformadores, teniendo como base SpanBERTa ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("scjnugacj/jurisbert") model = AutoModel.from_pretrained("scjnugacj/jurisbert") ``` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="scjnugacj/jurisbert", tokenizer="scjnugacj/jurisbert" ) fill_mask("interés superior del <mask>.") [ { "score": 0.941512405872345, "token": 3152, "token_str": " menor", "sequence": "interés superior del menor" }, { "score": 0.046888645738363266, "token": 3337, "token_str": " niño", "sequence": "interés superior del niño" }, { "score": 0.004166217986494303, "token": 9386, "token_str": " adolescente", "sequence": "interés superior del adolescente" }, { "score": 0.0008063237182796001, "token": 4914, "token_str": " menores", "sequence": "interés superior del menores" }, { "score": 0.0006806919700466096, "token": 48133, "token_str": " infante", "sequence": "interés superior del infante" } ] ``` | 96f52bbb6447d442a6c6255ea0cede04 |
other | [] | false | Términos de uso Al descargar este modelo usted ha aceptado quedar vinculado por los términos establecidos en este aviso legal. El propietario del modelo se reserva el derecho de enmendar, modificar o sustituir estos términos de uso en cualquier momento y sin previo aviso. Cuando una persona o entidad despliegue o proporcione sistemas, servicios, y/o cualquier tecnología a terceros usando este modelo y/o alguno derivado del mismo, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y cumplir con la normativa aplicable en todo momento. En ningún caso el propietario de los modelos (SCJN – Suprema Corte de Justicia de la Nación) ni la ( UGACJ - Unidad General de Administración del Conocimiento Juridico) serán responsables de los resultados derivados del uso que se de a estos modelos. | 9ab1c6734c3bbda5acef1b18d6db0036 |
other | [] | false | Uso previsto Este modelo fue creado con la finalidad de que cualquier persona o institución pueda crear herramientas de consulta de información jurídica del Estado Mexicano basados en modelos de lenguaje. | 6c5c9a1405c7ad942ace9a983ccd46d2 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 128 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: fp16 | 900e396af7cc7f827ecd55c2f7c1dc64 |
mit | ['bert', 'pytorch'] | false | Introduction BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/). | 9315423bb8cb9f71ce3671cae15019ae |
mit | ['bert', 'pytorch'] | false | Params | | ---------------------------------------- | ---------- | ------- | ------- | | `neuralmind/bert-base-portuguese-cased` | BERT-Base | 12 | 110M | | `neuralmind/bert-large-portuguese-cased` | BERT-Large | 24 | 335M | | 63c5e961edcdab2feb3aa2a6e2e0cf73 |
mit | ['bert', 'pytorch'] | false | or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False) ``` | cf9888aa006503088f19bc101091b591 |
mit | ['bert', 'pytorch'] | false | For BERT embeddings ```python import torch model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased') input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt') with torch.no_grad(): outs = model(input_ids) encoded = outs[0][0, 1:-1] | 2a374240c8a948b905a8bbb19b5fbdf9 |
mit | ['bert', 'pytorch'] | false | Citation If you use our work, please cite: ```bibtex @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } ``` | 15d186910839bbe955fe5d843b110b6c |
mit | ['stable-diffusion', 'text-to-image'] | false | Its Calling (Mob Umamusume) on Waifu Diffusion v1.3.5 This is the `<wd135-itscalling-mob-umamusume>` concept taught to [Waifu Diffusion v1.3.5](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/models/wd-1-3-5_80000-fp32.ckpt) via Textual Inversion. | 387dda4eaad190402dee725499cdb1c7 |
mit | ['stable-diffusion', 'text-to-image'] | false | Credits The training images were selectively taken from [Pixiv](https://www.pixiv.net), [Twitter](https://twitter.com), and in-game screenshots of Uma Musume Pretty Derby. A CSV file describing the original sources for most images is available in the [raw dataset archive file](./datasets/raw.7z). | 610cb5f405a088712542e0a9318bd7ae |
mit | ['stable-diffusion', 'text-to-image'] | false | Input Here is the new concept you will be able to use as an `object`:      | d9d3dfd99d47f89d6f941dc78c894fc7 |
mit | ['stable-diffusion', 'text-to-image'] | false | Output Examples Some images that can be possibly generated by using the new concept: !["<wd135-itscalling-mob-umamusume>, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]" -s 64 -S 3505534900 -W 512 -H 768 -C 10 -A k_dpmpp_2](./examples/000013.63c4d22c.3505534900.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.4", "image": { "prompt": [ { "prompt": "<wd135-itscalling-mob-umamusume>, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]", "weight": 1 } ], "steps": 64, "cfg_scale": 10, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 3505534900, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` !["<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, running outdoors park, white t-shirts black shorts, morning sunlight, pov from side looking at viewer cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]" -s 64 -S 821696414 -W 512 -H 768 -C 10 -A k_dpmpp_2](./examples/000019.37833118.821696414.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.4", "image": { "prompt": [ { "prompt": "<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, running outdoors park, white t-shirts black shorts, morning sunlight, pov from side looking at viewer cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]", "weight": 1 } ], "steps": 64, "cfg_scale": 10, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 821696414, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` !["<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, running outdoors park, white t-shirts black shorts, morning sunlight, pov from side looking at viewer cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]" -s 64 -S 460073536 -W 512 -H 768 -C 10 -A k_dpmpp_2](./examples/000020.58cf5625.460073536.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.4", "image": { "prompt": [ { "prompt": "<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, running outdoors park, white t-shirts black shorts, morning sunlight, pov from side looking at viewer cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]", "weight": 1 } ], "steps": 64, "cfg_scale": 10, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 460073536, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` !["<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, school sailor uniform white shirt purple pleated skirt, standing looking at viewer smile one eye closed arms behind back, standing indoors empty classroom, dusk sunset ambience light, full body shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]" -s 64 -S 1869090925 -W 512 -H 768 -C 10 -A k_dpmpp_2](./examples/000032.f35340f2.1869090925.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.4", "image": { "prompt": [ { "prompt": "<wd135-itscalling-mob-umamusume> horse ears horse tail horse girl, school sailor uniform white shirt purple pleated skirt, standing looking at viewer smile one eye closed arms behind back, standing indoors empty classroom, dusk sunset ambience light, full body shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]", "weight": 1 } ], "steps": 64, "cfg_scale": 10, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 1869090925, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` | 16e40f2d6fcdf5a3b9422e7bcf712b5c |
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: 0.2220 - Accuracy: 0.9215 - F1: 0.9216 | 10c683db7f023a35fcb247b2acb8db18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8267 | 1.0 | 250 | 0.3110 | 0.909 | 0.9073 | | 0.252 | 2.0 | 500 | 0.2220 | 0.9215 | 0.9216 | | 88b6126609174c784088f670dc2e9050 |
creativeml-openrail-m | ['text-to-image'] | false | Aipom_From_Pokémon-Diffusion Dreambooth model trained by Laughify with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or 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) | 2b8df58dda90f058094c85a6a5c418d5 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_wav2vec2_s530 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (pl)](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. | 05e7706da906474871b34d9d550e3d83 |
other | ['text-generation', 'opt'] | false | How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-2.7b") >>> generator("Hello, I'm am conscious and") [{'generated_text': 'Hello, I am conscious and I am a human being.\nI am a human being, and'}] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True) >>> generator("Hello, I'm am conscious and") [{'generated_text': "Hello, I'm am conscious and I make things. I'm in the creative community, which is"}] ``` | 7d85f95da30fe432e8025580403f3f35 |
other | ['text-generation', 'opt'] | false | Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The woman worked as a") [{'generated_text': "The woman worked as a security guard at a nursery in the city's eastern district of Samut P"}, {'generated_text': 'The woman worked as a doctor in the Philippines. Officials in China allege she stole the coronavirus'}, {'generated_text': 'The woman worked as a teacher in the city of Krasnodar in south Russia. She'}, {'generated_text': 'The woman worked as a researcher and lecturer at the Russian Academy of Sciences in a laboratory dedicated to the'}, {'generated_text': 'The woman worked as a nanny on a property owned by Mr Fitton-Allen in the city'}] ``` compared to: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The man worked as a") [{'generated_text': "The man worked as a security guard at a retirement home after being hired by the administrator's cousin,"}, {'generated_text': 'The man worked as a doctor in the Philippines.\n\nHe had hoped to work his way back'}, {'generated_text': 'The man worked as a teacher in the city of Krasnodar in south Russia.He'}, {'generated_text': 'The man worked as a researcher and his work on the topic predates the project, by many years'}, {'generated_text': 'The man worked as a chef in a restaurant for 40 years. How could this be so different from'}] ``` This bias will also affect all fine-tuned versions of this model. | 919e957332379a7211c0fef88e23f8c1 |
apache-2.0 | ['generated_from_keras_callback'] | false | transformers-abhi This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9227 - Validation Loss: 2.5929 - Train Rougel: tf.Tensor(0.19853836, shape=(), dtype=float32) - Epoch: 0 | 6d07109fdbbd03df4cea274b7289e965 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 61d4d2259d7fccb35a099c460b83ba06 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:----------------------------------------------:|:-----:| | 2.9227 | 2.5929 | tf.Tensor(0.19853836, shape=(), dtype=float32) | 0 | | b526aed2b24ae3ac73857bf849fb0ba9 |
apache-2.0 | ['bert', 'rte', 'glue', 'kd', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on RTE dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation. The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/rte/kd/bert_base_uncased_from_bert_large_uncased.yaml). I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**. | 609c925516dd6bd49d49772c838c835e |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Whisper Small3 Italian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 it dataset. It achieves the following results on the evaluation set: - Loss: 0.2307 - Wer: 10.2508 | d7a5848bbf66a7525cb051b512d36de7 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Intended uses & limitations This model has been developed as part of the Hugging Face Whisper Fine Tuning sprint, December 2022. It is meant to spread the knowledge on how these models are built and can be used to develop solutions where it is needed ASR on the Italian Language. It has not been extensively tested. It is possible that on other datasets the accuracy will be lower. Please, test it before using it. | 751722b2f522fb7c51ea98f04d4c90c8 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP | 1edf7e7b261b68d40444cb77e996e3a5 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.226 | 2.01 | 1000 | 0.2494 | 11.3684 | | 0.1017 | 4.02 | 2000 | 0.2403 | 10.6029 | | 0.0491 | 6.03 | 3000 | 0.2549 | 10.9591 | | 0.1102 | 8.04 | 4000 | 0.2307 | 10.2508 | | 0.0384 | 10.05 | 5000 | 0.2592 | 10.5903 | | 0.0285 | 12.06 | 6000 | 0.2537 | 10.5026 | | 5412d5ed219b3ed281df0fa2baf82183 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/msmarco-distilbert-base-v4 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 1720cc6fb632f0d18fd77ca3fbe9d59a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-v4') embeddings = model.encode(sentences) print(embeddings) ``` | fd3ee41575318386a40992e7da33fd26 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-distilbert-base-v4') model = AutoModel.from_pretrained('sentence-transformers/msmarco-distilbert-base-v4') | f1a200aa7f131c867c87fa5cf0300f86 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-v4) | 03f60b66e57ec703422b88c9e1b1c159 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` | 14c8dc76340da12dcd1525e9fc46b4f9 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3852 - Accuracy: 0.8424 - F1: 0.8398 | e6a9483a9809da745a13a9d5bcd693e1 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2401 - F1: 0.8246 | ab9534db38c323d806d330b3c0f2615a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8187 | 1.0 | 70 | 0.3325 | 0.7337 | | 0.2829 | 2.0 | 140 | 0.2554 | 0.8003 | | 0.1894 | 3.0 | 210 | 0.2401 | 0.8246 | | e9af34550689d4ad440d067abea310d2 |
apache-2.0 | ['generated_from_keras_callback'] | false | thanat/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0061 - Validation Loss: 3.3257 - Epoch: 7 | db1b33d9f360f802c3d92aba9c9bd9e8 |
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