license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | 下游效果 Performance <table> <tr> <td rowspan=2>Language</td> <td rowspan=2>Method</td> <td colspan=3>Text-to-Image Retrival</td> <td colspan=3>Image-to-Text Retrival</td> <td rowspan=2>MR</td> </tr> <tr> <td>R@1</td> <td>R@5</td> <td>R@10</td> <td>R@1</td> ... | 0c94c285e341dda22803dccc2ea55fdd |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | 可视化效果 Visualization effects 基于AltCLIP，我们还开发了AltDiffusion模型，可视化效果如下。 Based on AltCLIP, we have also developed the AltDiffusion model, visualized as follows.  | 1cc6bfecc268fdf2970bc11078b42091 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | now our repo's in private, so we need `use_auth_token=True` model = AltCLIP.from_pretrained("BAAI/AltCLIP") processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=["a ph... | 5d94669b6a6b04f20569c3e3c1a6b53b |
mit | ['luxembourgish', 'lëtzebuergesch', 'text generation'] | false | LuxGPT-2 GPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. The training took place on a 32 GB Nvidia Tesla V100 - with an initial learning rate of... | 8521eb230b34da883538b7818b3f0ce8 |
mit | ['luxembourgish', 'lëtzebuergesch', 'text generation'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2") model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2") ``` | ddac91ccdbbb20ab65416bd875c08823 |
mit | ['luxembourgish', 'lëtzebuergesch', 'text generation'] | false | Limitations and Biases See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2. | 2ddbd952acc6f580355617bca31e338d |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'hter'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-hter-en_lv-it-smt", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict(... | 9350b94c88fde86b8268b401e4480de2 |
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: 0.2928 - F1: 0.7730 | eaa4c171dca8a00c2d8aeb06a74f9825 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.4548 | 1.0 | 27 | 0.6522 | 0.5457 | | 0.5214 | 2.0 | 54 | 0.3476 | 0.7404 | | 0.3186 | 3.0 | 81 | 0.2928 | 0.7730 | ... | 5866461147c7d12cfc74245630dd26c3 |
mit | ['generated_from_trainer'] | false | tomekkorbak/test-pii-2533 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-20... | 94a667d0fc3e2994be333276f447cc31 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedul... | 2f779a30bc9fa9887b41194a26f1e2a2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ac-2.1-512 Dreambooth model trained by AaronEC 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-di... | 8e4fdafd7412e94a5633a0acf2b07782 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'zh-HK'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ZH-HK dataset. It achieves the following results on the evaluation set: - Loss: 1.4848 - Wer: 0.8004 | 6f79d9655155d1377c42493ceed3ebd5 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'zh-HK'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | dbcb151636e5847855d00f967955289b |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'zh-HK'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 1.0 | 183 | 47.8442 | 1.0 | | No log | 2.0 | 366 | 6.3109 | 1.0 | | 41.8902 | 3.0 | 549 | 6.2392 | 1.0 ... | 44e7b7cb68089babd4ebbc5dcbcaf058 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'zh-HK'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id ivanlau/wav2vec2-large-xls-r-300m-cantonese --dataset mozilla-foundation/common_voice_8_0 --config zh-HK --split test --log_outputs ``` 2. To evaluate on `speech-recognition-community-v2/de... | 65c7147dd6b7123f574581ac7276f609 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_qnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6621 - Accuracy: 0.6002 | 8a18d3347b0c03906761e37fe91ee880 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6894 | 1.0 | 410 | 0.6660 | 0.5933 | | 0.6593 | 2.0 | 820 | 0.6621 | 0.6002 | | 0.6441 | 3.0 | 1230 | 0.6634 | 0.... | a71910d68ca12b0a2fbde38d48e51200 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-small-coref This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the winograd_wsc dataset. The model was trained on the task of coreference resolution. It achieves the following results on the evaluation set: - Loss: 0.5656 - Rouge1: 0.906 - Rouge2... | f74f3da5c64eeedc53c5b2858fa2c456 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 16 | 1.0901 | 0.6849 | 0.561 | 0.6734 | 0.6746 | 18.4483 | |... | bae18f5c9541d0b2dfc7acd1469db78c |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 7bce3e978fa88ec747a8544b7db1b809 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3335 - Accuracy: 0.8633 - F1: 0.8664 | 1b008824f8922880905cf6fda095093a |
apache-2.0 | ['translation'] | false | opus-mt-en-guw * source languages: en * target languages: guw * OPUS readme: [en-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-guw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | fc020025a52663b26739128c025b407f |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-5-16-5 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: 2.3560 - Accuracy: 0.2692 | 5015f3609424d717ad9a361d100d1115 |
mit | [] | false | kaltsit_v2 on Stable Diffusion via Dreambooth This your the Stable Diffusion model fine-tuned the kaltsit_v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **kaltsit** v2 update: 1. increase sample size. more stable results. 2. prompt update: kaltsit. 3. prior ... | 904ac440ec0c77f437b562f8c0fe9167 |
mit | ['javanese-bert-small-imdb'] | false | Javanese BERT Small IMDB Javanese BERT Small IMDB is a masked language model based on the [BERT model](https://arxiv.org/abs/1810.04805). It was trained on Javanese IMDB movie reviews. The model was originally the pretrained [Javanese BERT Small model](https://huggingface.co/w11wo/javanese-bert-small) and is later fi... | 177055bd8bcfde914672ccc2bfc393f8 |
mit | ['javanese-bert-small-imdb'] | false | params | Arch. | Training/Validation data (text) | |----------------------------|----------|----------------|---------------------------------| | `javanese-bert-small-imdb` | 110M | BERT Small | Javanese IMDB (47.5 MB of text) | | f955947909d9f0ac87d8cae2180749c6 |
mit | ['javanese-bert-small-imdb'] | false | Evaluation Results The model was trained for 5 epochs and the following is the final result once the training ended. | train loss | valid loss | perplexity | total time | |------------|------------|------------|-------------| | 3.070 | 2.989 | 19.87 | 3:12:33 | | 549aca0462c099b6a8395957df1ac794 |
mit | ['javanese-bert-small-imdb'] | false | As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/javanese-bert-small-imdb" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Aku mangan sate ing [MASK] bareng konco-konco") ``` | 78b0d5fc8c529d1fd216f3ffabb523a0 |
mit | ['javanese-bert-small-imdb'] | false | Feature Extraction in PyTorch ```python from transformers import BertModel, BertTokenizerFast pretrained_name = "w11wo/javanese-bert-small-imdb" model = BertModel.from_pretrained(pretrained_name) tokenizer = BertTokenizerFast.from_pretrained(pretrained_name) prompt = "Indonesia minangka negara gedhe." encoded_input ... | 3051da6c5e2e2fe11c6c94395d7c9228 |
other | ['text-generation', 'opt'] | false | Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with the... | f7497460ec820cd14ebdc8c7ed82defc |
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-125m") >>> generator("Hello, I'm am conscious and") [{'generated_text': 'Hello, I am conscious and aware of the fact that I ... | 7196a843a43366491f71b9a02065bf4f |
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... | 5aa3d563bf643e0dc8b0aa52728cdbd3 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT-SurgicalCardiothoracic This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8426 | 399064b563c8e604655afcd0d16ecedd |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | 86f7ada63f4235aa7d5d06ba0e2e6027 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 13144 | 0.9092 | | No log | 2.0 | 26288 | 0.8575 | | No log | 3.0 | 39432 | 0.8417 | | ef9d9bc5fddaab623e1b7154d72fbcd7 |
apache-2.0 | ['translation'] | false | opus-mt-en-ty * source languages: en * target languages: ty * OPUS readme: [en-ty](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ty/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 20fe990de968789aed7c54673f5dea27 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_lists-roberta-large-v1-5-93 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://ww... | f5cc8c02552f33eeeabcfa0f92b419bd |
mit | ['generated_from_keras_callback'] | false | deepiit98/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5009 - Train End Logits Accuracy: 0.8715 - Train Start... | 90d60e262a05ba8ae59b1723cabf4d89 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 0d3de796f1ed9158da19094a28be7bf6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-t5-Thor4 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: - Loss: 1.5607 - Rouge1: 30.1917 - Rouge2: 17.6334 - Rougel: 26.8513 - Rougelsum: 28.7606 - Gen Len: 18.9881 | f3321bf1caf3efb08e5a330d05740745 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9251 | 1.0 | 675 | 1.6082 | 29.3372 | 16.9607 | 26.1096 | 27.9357 | 18... | 5373d639589f730d09556ad77370f6be |
apache-2.0 | ['generated_from_trainer', 'bem', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-bemba-fds This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [BembaSpeech](https://github.com/csikasote/BembaSpeech) dataset. It achieves the following results on the evaluation set: - Loss: 0.3594 - Wer: 0.3838 | 976d89ceae1e4b30794e59d8d47fe547 |
apache-2.0 | ['generated_from_trainer', 'bem', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9961 | 0.67 | 500 | 0.5157 | 0.7133 | | 0.5903 | 1.34 | 1000 | 0.3663 | 0.4989 | | 0.4804 | 2.02 | 1500 | 0.3547 | 0.4653 | |... | 8e62793306756d79103712585e526df9 |
gpl-3.0 | ['audio', 'automatic-speech-recognition', 'endpoints-template'] | false | Video Search This project contains 3 different models that can be used for searching videos. 1. Whisper to convert mp3 files to audio 2. BART Sentence Transformer to generate vector embeddings from text 3. BART LFQA to generate long form answers given a context For more context, see: [Atlas: Find Anything on Youtub... | 308e8d1c51f02fe6c4fa5f50f7239ae9 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-8_austria-2_s42 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 sur... | 68e520e412a2db10e63c952e24631957 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_xlsr-53_s973 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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... | 1f8e55fc50fbae4f42a68f69e0b0d09c |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.03-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8139 - Bleu: 7.2039 - Gen Len: 44.4967 | 130a9dc6350a492c9b8e96bcbcb5f3c6 |
apache-2.0 | ['exbert'] | false | CorefBERT large model Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in [this paper](https://arxiv.org/abs/2004.06870) and first released in [this repository](https://github.com/thunlp/CorefBERT). Disclaimer: The team r... | 65d790cc633483c1951671b83a39a3e2 |
apache-2.0 | ['exbert'] | false | Model description CorefBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate in... | a1470ce3e29b1a7d3a430f3d88c00f3c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-german-cased-finetuned-tagesschau-subcategories This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5230 - Accuracy: 0.8267 | 5e54d5cdc39d43ba20c02772100ed804 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.4 | 30 | 1.5130 | 0.5733 | | No log | 0.8 | 60 | 1.0629 | 0.7133 | | No log | 1.2 | 90 | 0.8431 | 0.... | 32e60ed0ec081b7f3536255e37c0cc68 |
apache-2.0 | ['generated_from_keras_callback'] | false | avialfont/dummy-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8606 - Validation Loss: 2.5865 - Epoch: 0 | 825ca45b575060d015ee15ca8e0c6808 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-8-9 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: 0.6925 - Accuracy: 0.5140 | 53c6f215d6f68ce2a63243b092111302 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7204 | 1.0 | 3 | 0.7025 | 0.5 | | 0.6885 | 2.0 | 6 | 0.7145 | 0.5 | | 0.6662 | 3.0 | 9 | 0.7222 | 0.... | 1168772163de605960a8a89b93698c4c |
apache-2.0 | ['splinter', 'SplinterModel'] | false | Splinter base model (with pretrained QASS-layer weights) Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter).... | 05f4a8c5301f6bfb270a3a0c4546df4e |
apache-2.0 | ['splinter', 'SplinterModel'] | false | Model description Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and... | 5aff53fd8967f12f71aa1931e5dc09f8 |
apache-2.0 | ['splinter', 'SplinterModel'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ram-etal-2021-shot, title = "Few-Shot Question Answering by Pretraining Span Selection", author = "Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer", booktitle = "Proceedings of the 59th... | 70d9e1aeaba18f22843c87a2b919f08f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 35.0 | db98a4dbcd67e170b66c8717433620c1 |
mit | [] | false | Stats In addition to the recently released [German BERT](https://deepset.ai/german-bert) model by [deepset](https://deepset.ai/) we provide another German-language model. The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This... | deb68d828442f8f5d155e9d46cd065eb |
mit | [] | false | Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | -------------------------------- | ---------------------------------... | abbd07203f39afef18ba137917756b3a |
mit | [] | false | Usage With Transformers >= 2.3 our German BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased") ``` | 68b5685c7fa9e3fde6b3ebff40b0dbc6 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_wav2vec2_s764 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 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 0753b79fd36f0f009c12c5144edf7c19 |
apache-2.0 | ['generated_from_keras_callback'] | false | BobBraico/rlb-cyber-finetuned-cyber This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7822 - Validation Loss: 2.4283 - Epoch: 0 | 59d6c11b6a3dfd9d939617395ef4d209 |
mit | ['generated_from_keras_callback'] | false | turkishReviews-ds-finetuned This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/turkishReviews-ds) on an unknown dataset. It achieves the following results on the evaluation set: | 1907b57abcc43d3caae8e161b47fe1af |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | c80cc1f4fa0574648e53b41bbfaaf401 |
['mit'] | [] | false | Citation For attribution in academic contexts, please cite this work as: ``` @mastersthesis{louis2020netbert, title={NetBERT: A Pre-trained Language Representation Model for Computer Networking}, author={Louis, Antoine}, year={2020}, school={University of Liege} } ``` | 52b51ec0a17dbe6d8c73aa5f29a551d5 |
mit | ['spanish'] | false | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Spanish embeddings left. * The original model has 582M parameters, with 237M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Spanish tokens) ... | 28e16c03511e0728d1d728e83125699f |
mit | ['spanish'] | false | Citing & Authors - Datasets : [cleaned corpora](https://github.com/crscardellino/sbwce) - Model : [google/mt5-base](https://huggingface.co/google/mt5-base) - Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base) | 402214a9fe099964047416d326f5dca5 |
mit | [] | false | T5 One Line Summary A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using [simpleT5](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 model... | 65a24fde36eb58759fa19873cb53b76a |
mit | [] | false | Usage:[](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing) ```python abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving pr... | f64f9b9be3d9f10ee42529597d7f6c73 |
mit | [] | false | Using Transformers🤗 ```python model_name = "snrspeaks/t5-one-line-summary" from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) input_ids = tokenizer.encode("summarize: " + abstract, return_tensor... | d3c309fd352216ea95132ac33cbee76b |
creativeml-openrail-m | ['text-to-image'] | false | model by kingery This your the Stable Diffusion model fine-tuned the zrn_01_sdv1-5_1e_6_1500_woman_ddim concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of yangguangkechuang woman** You can also train your own concepts and upload them to the library by u... | e431a847826b714896cf05be7636be11 |
mit | [] | false | HOI4 Leaders on Stable Diffusion This is the `<HOI4-Leader>` 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 a... | 5ac09d8875edb60786b32c4f3bcd6515 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/distilbert-base-uncased-finetuned-center_allsides_news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0800 - Validation Loss: 2.0824 - Epoch: 9 | 43e11db095167f20f276fd4db9cfb1c7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 529a60652f59e77b961a04393bb744cf |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.2499 | 2.2541 | 0 | | 2.2421 | 2.0901 | 1 | | 2.2062 | 2.0876 | 2 | | 2.2091 | 2.1683 | 3 | | 2.1944 | 2.0739 | 4 | | 2.1631 |... | a6365ca2ec05a92381545e081a95bc1b |
apache-2.0 | ['pubmed', 'cancer', 'gene', 'clinical trial', 'bioinformatic'] | false | Roberta-Base fine-tuned on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) Abstract > We limit the training textual data to the following [MeSH](https://www.ncbi.nlm.nih.gov/mesh/) * All the child MeSH of ```Biomarkers, Tumor(D014408)```, including things like ```Carcinoembryonic Antigen(D002272)``` * All the child MeSH of... | af3b6668ce4d3b54577e0b3f8fd3de2d |
apache-2.0 | ['pubmed', 'cancer', 'gene', 'clinical trial', 'bioinformatic'] | false | select model path for checkpoint overwrite_output_dir=True, num_train_epochs=3, per_device_train_batch_size=30, per_device_eval_batch_size=60, evaluation_strategy= 'steps', save_total_limit=2, eval_steps=250, metric_for_best_model='eval_loss', greater_is_better=False, load_best_m... | 9adfe55cc6b07dc6ab7ccfe4ed41e118 |
mit | ['generated_from_keras_callback'] | false | Deep98/Pub-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3841 - Train End Logits Accuracy: 0.8993 - Train Start Logits Accuracy: 0.857... | 5ba3b38c56373436dc755c2af45482fd |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | ab87cc94e3b136131794b6abcd7a78f0 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | huanglongyidou Dreambooth model trained by jiaheillu Sample pictures of this concept:   on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.8530 - Validation Loss: 10.7406 - Epoch: 2 | 188cc82d274e0e471c38753060be48ac |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | c7555bb78bd9b4705fab43f6f0fb863a |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.9815 | 10.9683 | 0 | | 10.9422 | 10.8815 | 1 | | 10.8530 | 10.7406 | 2 | | 67bd115563e93da9dbdbaa515abdce16 |
creativeml-openrail-m | [] | false | --- license: creativeml-openrail-m --- This model is dreamboothed on four concepts from yamanosusume **Prompts:** 1. aohina yuri kurauehinata yukimuraaoi 2girls 2. yukimuraaoi girl 3. kurauehinata girl 4. aobakokona girl --- **Training details:** - Trained with [TheLastBen's fast-DreamBooth notebook](https://col... | b2e915cef2513e25dcc66a0215d24baa |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-v3-e4 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7948 - Rouge1: 52.8917 - Rouge2: 33.9404 - Ro... | 3b13b7289f792bcabc2ad8e905ae10b9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9591 | 52.9984 | 33.2737 | 34.5312 | 50.3676 | ... | 9187f7b59b4146768bad961c3598d4a1 |
apache-2.0 | ['translation'] | false | opus-mt-fi-tr * source languages: fi * target languages: tr * OPUS readme: [fi-tr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-tr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-04-12.zip](https://... | d66efb91a990e21c736a237ff558abb5 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-de-en-finetuned-de-to-en-second This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2282 - Bleu: 37.9762 - Gen Len: 25.3696 | 19438e7f00db5eb851901a3641ebc876 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 157 | 1.1837 | 38.8278 | 25.22 | | No log | 2.0 | 314 | 1.2057 | 38.3047 | 25.2908 | | No log |... | ee4baa3c6799f2d67aad7c481a38de8b |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-es_s281 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 6c1cc179ff6e438e3af84ac0d658dadc |
apache-2.0 | ['translation'] | false | opus-mt-es-NORWAY * source languages: es * target languages: nb_NO,nb,nn_NO,nn,nog,no_nb,no * OPUS readme: [es-nb_NO+nb+nn_NO+nn+nog+no_nb+no](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-nb_NO+nb+nn_NO+nn+nog+no_nb+no/README.md) * dataset: opus * model: transformer-align * pre-processing: n... | 1957edf6a21711b4b5179fc67ea8448b |
apache-2.0 | ['generated_from_trainer'] | false | IMDB_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2220 - Accuracy: 0.9467 | 91ad89c7ed0c2930e1771a30afef9bf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 8b02655b27f31f3a7f04f900023ff3f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5285 | 0.06 | 50 | 0.2692 | 0.9133 | | 0.3515 | 0.13 | 100 | 0.2054 | 0.9267 | | 0.2314 | 0.19 | 150 | 0.1669 | 0.... | 9b80f3bf246467a75523cdc09cde0b39 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]") processor = Wav2Vec2Processor.from_pr... | 9db830167298b6894ba08661291ecc6a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Punjabi test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pa-IN", split="test") wer ... | ab80404d1d0aa732001744da8a1e8a7e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | eb6ed8e9141f7ffbcbd9c32c2e764f77 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | MAXIM pre-trained on FiveK for image retouching MAXIM model pre-trained for image retouching. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first release... | 77310b2b02a8635921583e399e3ff3ff |
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