license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
gpl-3.0 | ['segmentation'] | false | Training Data The GLENDA "no pathology" dataset was used to train the model: * [GLENDA Dataset](http://ftp.itec.aau.at/datasets/GLENDA/), which contains ~12k image frames. * Masks (to be released), were generated using the specular reflection detection pipeline found in this paper (to be released). * Train/Val/Tes... | 644359b6e1c494ade8661595d81858f4 |
gpl-3.0 | ['segmentation'] | false | Training and Evaluation Procedure & Results You can view the training logs [here at Weights and Biases](https://wandb.ai/nano-1337/Predict/reports/SpecLab-Training-for-10-Epochs--VmlldzoyNDYyNDIz?accessToken=xfjtfgb5szvsk08luvmwinjl6y2kvp1vl1eax52kbxgwgbwjqv29yed9elzgbju1) During training, input images pass through ... | 1ce66a5c61d32412b21d947d655326a0 |
gpl-3.0 | ['segmentation'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. * **Hardware Type:** Tesla V100-SXM2 * **Hours used:** 6 * **Cloud Provider:** Google Colab * **Compute Region:** us-south1 * **Carbon... | 801b96c06e8164bc88d96ba81aedec07 |
gpl-3.0 | ['segmentation'] | false | Citation ```bibtext @misc{Yin_SpecLab_2022, author = {Yin, Haoli}, doi = {TBD}, month = {8}, title = {SpecLab}, url = {https://github.com/Nano1337/SpecLab}, year = {2022} } ``` *This model card was written by: Haoli Yin* | 2e2d380f7d2392884ad32a9a9fee76c3 |
mit | ['question-generation'] | false | T5 for multi-task QA and QG This is multi-task [t5-small](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate ques... | 526bc51eec15ba335846e2380f442cb9 |
mit | ['question-generation'] | false | Model in action 🚀 You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) ```python3 from pipelin... | a61ba056bd293e236abdfacee1dbf2d6 |
mit | ['question-generation'] | false | to generate questions simply pass the text nlp("42 is the answer to life, the universe and everything.") => [{'answer': '42', 'question': 'What is the answer to life, the universe and everything?'}] | 34a149f393c293206cb3237fb90aad04 |
apache-2.0 | ['generated_from_trainer'] | false | electra-small-discriminator-CoLA This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4403 - Matthews Correlation: 0.5510 | 06f47e263da5ac582bde2f9142d5666a |
apache-2.0 | ['generated_from_trainer'] | false | Model description trying to optimize accuracy/speed: ```json { "epoch": 8.0, "eval_loss": 0.4402828514575958, "eval_matthews_correlation": 0.5510400717227824, "eval_runtime": 0.9341, "eval_samples": 1043, "eval_samples_per_second": 1116.545, "eval_steps_per_second": 70.654 } ``` | 0ec3af014abaa652dd8757ecb9ebc930 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 512 - eval_batch_size: 16 - seed: 32754 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - ... | 079932d8e1d6d62f8f4720487a75a5bb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6139 | 1.0 | 17 | 0.5997 | 0.0 | | 0.5315 | 2.0 | 34 | 0.4890 | 0.5154 | | 0.4... | 52276b5f566a558c70c516c8ec556f84 |
apache-2.0 | ['translation'] | false | opus-mt-fi-el * source languages: fi * target languages: el * OPUS readme: [fi-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-el/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 843ef3abffe7dd8e8e65966807dc9868 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_wav2vec2_s250 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 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 8a5d802fcfb96710f737c19c692cd24e |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-2_female-8_s659 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | e7cc7b6ffc28ec201ec4b9797f51a957 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mbart-large-cc25-ruquad-qag` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question & answer pair generation task on the [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (dataset_name: default) via [`lmqg`](ht... | 1c0cc7e7e6bf565405c732ed89eb0eb0 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** ru - **Training data:** [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https:/... | 78acba4f47203f607a1f7bc38ac31987 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.") ``` - With `transformers` ```python from transformers im... | 4d9b0233fc06654f342c5507902f519c |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json) | | Score | Type | Dataset ... | 51a0036590b01114551e00acc98dd90d |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_ruquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 256 - ... | ceb276088087932de47aafa99d023e16 |
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: 0.1599 - Accuracy: 0.934 - F1: 0.9341 | e1e16fc0d70e837d69157c43431f6a3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1887 | 1.0 | 250 | 0.1806 | 0.9295 | 0.9293 | | 0.1245 | 2.0 | 500 | 0.1599 | 0.934 | 0.9341 | | 3d0fb6f86d33d507f2e9abe90fe2a63f |
apache-2.0 | ['translation'] | false | opus-mt-mfs-es * source languages: mfs * target languages: es * OPUS readme: [mfs-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mfs-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | ac64a173bfd60cf5897d7407df248360 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1753 - F1: 0.8520 | 155fde81bcc007ef267cd051a1c415fb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2989 | 1.0 | 835 | 0.1878 | 0.8123 | | 0.1548 | 2.0 | 1670 | 0.1745 | 0.8480 | | 0.1012 | 3.0 | 2505 | 0.1753 | 0.8520 | ... | 81beff5d4a14c85ea1717c8134a2ab71 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-FF2000 (Deep-Narrow version) T5-Efficient-BASE-FF2000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoin... | 0227f87e5e09086bacae8b2df42056f3 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-ff2000** - is of model type **Base** with the following variations: - **ff** is **2000** It has **185.18** million parameters and thus requires *ca.* **740.73 MB** of memory in full precision (*fp32*) or **370.37 MB** of memory in half precisio... | 8b0fc339034273cd374eae18ad5984a4 |
apache-2.0 | ['translation'] | false | opus-mt-en-kj * source languages: en * target languages: kj * OPUS readme: [en-kj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-kj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 289d6e8b9fb5da7f61fe7422c87c43eb |
mit | [] | false | yoji-shinkawa-style" on Stable Diffusion This is the `<yoji-shinkawa>` 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.... | 74788f81389e471b7f06efb66530c7f8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-ukrainian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 05b9e26df138562c24810be388f39ea3 |
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", "uk", split="test[:2%]"). processor = Wav2Vec2Processor.from_... | da3bf38ba13df6491dc51350c53b8cdd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Ukrainian 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", "uk", split="test") w... | fd5d1ac9cf6947f0204375f5a73f09f3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(log... | 64232cc002b04fb5d3d0d055289b3197 |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | Legal BERT model applicable for Dutch and English A BERT model further trained from [mBERT](https://huggingface.co/bert-base-multilingual-uncased) on legal documents. The thesis can be downloaded [here](https://www.ru.nl/publish/pages/769526/gerwin_de_kruijf.pdf). | 262c0b4864c3de7217096e316859e4b6 |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | Data The model is further trained the same way as [EurlexBERT](https://huggingface.co/nlpaueb/bert-base-uncased-eurlex): regulations, decisions, directives, and parliamentary questions were acquired in both Dutch and English. A total of 184k documents, around 295M words, was used to further train the model. This is le... | 3a542b4454c82c92a44b331f60823925 |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | How to use ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("Gerwin/legal-bert-dutch-english") model = AutoModel.from_pretrained("Gerwin/legal-bert-dutch-english") | 112c7840898c0f4d6371687e82091964 |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | Benchmarks Here are a couple of comparisons between popular BERT models and this model. The fine-tuning procedures for these benchmarks are identical for each pre-trained model, and are more explained in the thesis. You may be able to achieve higher scores for individual models by optimizing fine-tuning procedures. Th... | 26631d2dd841bb0e56ac3e417b64f85f |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | Legal topic classification | Model | [Multi-EURLEX (NL)](https://huggingface.co/datasets/multi_eurlex) | | ----------------------------------------------------------------------------- | ------------------------------------------------------------... | 26b8ef96ccff6d91ea3733fc93cb21d1 |
apache-2.0 | ['bert', 'legal', 'multilingual'] | false | Multi-class classification (Rabobank) This dataset is not open-source, but it is still an interesting case since the dataset contains both Dutch and English legal documents that have to be classified. The dataset consists of 8000 long legal documents (2000 Dutch & 6000 English) with a total of 30 classes. Using a comb... | 03be1d002fbd3cb39494d08aa99196b2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'art', 'style'] | false | 💞 Send me Query at : [](https://www.instagram.com/iamhemantindia) You can test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusi... | 926a6ad51362d215df703e94f5072370 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-aprischa2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3425 - Rouge1: 65.7088 - Rouge2: 56.6701 - Rougel: 62.1926 - Rougelsum: 64.7727 - Gen Len: ... | beaf0cc2e24ac3ddc9791fac49c2d621 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 243f1ebaf69bf150d5942315df94a1ae |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.3772 | 1.0 | 5403 | 0.3586 | 65.7702 | 56.7968 | 62.264 | 64.8605 ... | c8455210bb6d394414b16663efdeb2e8 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0738 - Precision: 0.9232 - Recall: 0.9437 - F1: 0.9333 - Accuracy: 0.9825 | e409d4de5f74caba542bba08f26eb852 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1397 | 1.0 | 1368 | 0.0957 | 0.9141 | 0.9048 | 0.9094 | 0.9753 | | 0.0793 | 2.0 |... | 985333f1098de02c20b6e30c7cf9c04d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0017 - eval_accuracy: 0.7477 - eval_runtime: 0.3985 - eval_samples_p... | 71b5b179c256b65265577fdce0a54cc4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.708803333901887e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native ... | fbe3a93c4e26c2e24b2fe3d95acdd73f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-indomain-mix This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6467 - Accuracy: 0.806 - F1: 0.7820 - Precision: 0.8923 - Recall: 0.696 - Mae: 0.194 -... | 059dc2012e507b30c2d9549c89e3d866 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:--:|:---:|:---:| | 0.3643 | 1.0 | 2714 | 0.6727 | 0... | 7132121a204a586d0716a6ce5522b679 |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization'] | false | MTL-summarization The MTL-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MV... | edff4b7d7497e066ff289b49c0126c81 |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization'] | false | Model Description MTL-summarization is supervised pre-trained using a mixture of labeled summarization datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture. MTL-summarization is specially designed for summarizat... | b551f8f0f24692ae3246eac87d0006ae |
apache-2.0 | ['text-generation', 'text2text-generation', 'summarization'] | false | Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-summarization") >>> inputs = tokenizer( ... "Summarize: You may want to stick it to your boss... | b077087b26c6ed008667e83eeabca1e7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-qnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Accuracy: 0.9087 | 251a268d0069c9268fa6cea1aaba3f5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6529 | 0.08 | 500 | 0.4945 | 0.7734 | | 0.4731 | 0.15 | 1000 | 0.3888 | 0.8406 | | 0.4113 | 0.23 | 1500 | 0.3605 ... | dd02a8fd05b1e3623572b0015c377bcc |
apache-2.0 | ['translation'] | false | opus-mt-es-nl * source languages: es * target languages: nl * OPUS readme: [es-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-nl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 79850e288ddff38df7029496d4750b7b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | noggles_v21_3400_30percent Dreambooth model trained by alxdfy 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... | 4b569b06b30d37a09e7ff870be2205c2 |
apache-2.0 | ['generated_from_trainer'] | false | distilhubert-finetuned-music-genres-small This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6827 - Accuracy: 0.4 | dc033e0907af357c8fd4c52f578d84b0 |
apache-2.0 | ['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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | a8f0708d3b53484abb2b5138cd21c6b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.99 | 56 | 2.0784 | 0.36 | | 2.098 | 1.99 | 112 | 1.8533 | 0.35 | | 2.098 | 2.99 | 168 | 1.7524 | 0.... | 88f37298db805b73275c7a99358cd47b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_model_fine_tuned_unlabeled This model is a fine-tuned version of [nouman-10/distilbert-base-uncased-finetuned-unlabeled](https://huggingface.co/nouman-10/distilbert-base-uncased-finetuned-unlabeled) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0157 - eval_acc... | d52ce4eaa2802ca2cf6e3eca16939de9 |
apache-2.0 | ['generated_from_trainer'] | false | fnet-large-finetuned-wnli This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6953 - Accuracy: 0.3803 | 7c4a4d5cfab2ad61594a1e2f983267fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7217 | 1.0 | 159 | 0.6864 | 0.5634 | | 0.7056 | 2.0 | 318 | 0.6869 | 0.5634 | | 0.706 | 3.0 | 477 | 0.6875 | 0.... | 48b2e4acd2b0618e27df31a6111edbb6 |
gpl | [] | false | Introduction We have scrapped all the collected works of Mohandas Karamchand Gandhi (aka Mahatma Gandhi) from [here](http://www.gandhiashramsevagram.org/gandhi-literature/collected-works-of-mahatma-gandhi-volume-1-to-98.php). Cleaned the text so that it contains only the writings of Gandhi without footnotes, titles, ... | af5fabf39343e1e4a94ee5c9cde8bb55 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | 86628fb7a70888696df78780500c4a0b |
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", "id", split="test[:2%]"). processor = Wav2Vec2Processor.from_pret... | cc9df1b3c2c9f0f8608ded4816e68709 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2... | 6056f2dc15af2591564f214191cc83d0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} 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", "id", split="test") wer ... | a931c5beda12f342c5c54563fd4a7019 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampl... | bcaa241ab0dea99b044c98223b364114 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logi... | fcd65785cf04404413a5e8bc9519894b |
mit | ['generated_from_trainer'] | false | yes_no_qna_deberta_model This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5570 - Accuracy: 0.8508 | a5791b6762461062d09b0a854f87c390 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.583 | 1.0 | 590 | 0.4086 | 0.8251 | | 0.348 | 2.0 | 1180 | 0.4170 | 0.8465 | | 0.2183 | 3.0 | 1770 | 0.5570 | 0.... | d827a0a465b2fa67e502e7ae879f73d7 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NH32 (Deep-Narrow version) T5-Efficient-BASE-NH32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | 9c615d2799f51005b42e6ef9d0f284d4 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nh32** - is of model type **Base** with the following variations: - **nh** is **32** It has **364.49** million parameters and thus requires *ca.* **1457.96 MB** of memory in full precision (*fp32*) or **728.98 MB** of memory in half precision (... | 87c5ee4b1800ebc70576e5d1b173bfc3 |
mit | ['AMRBART'] | false | AMRBART-large-finetuned-AMR3.0-AMR2Text This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a sacre-bleu score of 45.0 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](ht... | f9bd4248a50994bf51ab6578a12f3c7e |
mit | ['AMRBART'] | false | How to use Here is how to initialize this model in PyTorch: ```python from transformers import BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMR2Text") ``` Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initi... | f24c721cdb4734180280203b5416a4ee |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b5-segments-warehouse1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the jakka/warehouse_part1 dataset. It achieves the following results on the evaluation set: - Loss: 0.1610 - Mean Iou: 0.6952 - Mean Accuracy: 0.8014 - Overall Accuracy: 0.9648 - Per Categor... | 08af7d24155d6061d985e85901545672 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | 98ae15050fb2b63c53d09bd34cd97765 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | bdaf28c45b08d43ec51b19de7cf2430e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-16 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1016 - Wer: 1.0 | 274b413222e5cdad2d4654d4c536aed3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | 35ba967d7abb3a5c6a6be8b49e325ca6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.6682 | 1.37 | 200 | 3.3138 | 1.0 | | 2.8751 | 2.74 | 400 | 2.9984 | 1.0 | | 2.8697 | 4.11 | 600 | 3.0827 | 1.0 | | 2.866 ... | 12e7bd949d67c7e78d1885feb7dfb2ed |
apache-2.0 | ['summarization'] | false | Introduction Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e. text or image) or limited multi-modal data (i.e. image-text pairs). In this work, we propose a unified-modal pre-training archi... | d991646dae8044cbcde67e99b0909d7a |
apache-2.0 | ['summarization'] | false | Available Models - **unimo-text-1.0**, *12 layer, 12 heads, 768 hidden size, pretrained model* - **unimo-text-1.0-large**, *24 layer, 16 heads, 1024 hidden size, pretrained model* - **unimo-text-1.0-lcsts-new**, *12 layer, 12 heads, 768 hidden size, finetuned on the lcsts-new Chinese summarization dataset* - **unimo-... | 96e2d8b4cd715cb42e8a84c61d0636c7 |
apache-2.0 | ['summarization'] | false | Citation Info ```text @article{ernie2.0, title = {UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning}, author = {Li, Wei and Gao, Can and Niu, Guocheng and Xiao, Xinyan and Liu, Hao and Liu, Jiachen and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:2012.15... | 296f18d49a9ca8330f8bb429c276cbe6 |
apache-2.0 | ['stable-diffusion', 'text-to-image'] | false | StableDiffusionLongPromptWeightingPipeline Pipeline for text-to-image and image-to-image generation using Stable Diffusion, without tokens length limit and support parsing weighting in prompt. require diffusers>=0.10.0 > Now the pipeline has been contributed to the official diffusers community pipelines. You can us... | 5347720ddc13c28a638d31d025539ca0 |
apache-2.0 | ['stable-diffusion', 'text-to-image'] | false | Acknowledgments Some code borrows from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui): https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/3246a2d6b898da6a98fe9df4dc67944635a41bd3/modules/sd_hijack_clip.py https://github.com/AUTOMATIC1111/stable-diffusion-we... | 7888a78c363cdd32f5f9b3ebc55dc1b2 |
cc-by-4.0 | [] | false | Model description EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.org/anthology/W13-2412.pdf). It can recognize three types of entities: locations (LOC), organizations (ORG) and... | bb5607476753aa19c1244aa5db34641b |
cc-by-4.0 | [] | false | How to use You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. ``` from transformers import BertTokenizer, BertForTokenClassification from transformers import pipeline tokenizer = BertTokenizer.from_pretr... | 5b5b453c6fdd357691b18abe8b7ac68f |
cc-by-4.0 | [] | false | BibTeX entry and citation info ``` @misc{tanvir2020estbert, title={EstBERT: A Pretrained Language-Specific BERT for Estonian}, author={Hasan Tanvir and Claudia Kittask and Kairit Sirts}, year={2020}, eprint={2011.04784}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | eb06db4ada92e21f7155838c49368dd5 |
mit | ['roberta-base', 'roberta-base-epoch_66'] | false | RoBERTa, Intermediate Checkpoint - Epoch 66 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 26a35b1d8029b2af292933ea3873f997 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | DeathNote Diffusion <p> <img src="https://huggingface.co/Guizmus/DeathNote/raw/main/samples/showcase_dn_2.jpg"/><br/> This is the fine-tuned Stable Diffusion model trained on images from the anime Death Note.<br/> The total dataset is made of 93 pictures, and the training has been done on naclbit/trinart_stable_dif... | 60e086a4d6ad6929360ebc7c3c1e51ee |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 089e0eefebb34a4260148b49091c5bbe |
afl-3.0 | ['image-classification', 'resnet'] | false | Intended uses
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).
| 46512530fc9281a2350c3add835f478c |
apache-2.0 | ['korean'] | false | KoELECTRA v2 (Base Generator) Pretrained ELECTRA Language Model for Korean (`koelectra-base-v2-generator`) For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md). | d58e685784a312e1ad91520f9de76f7d |
apache-2.0 | ['korean'] | false | Load model and tokenizer ```python >>> from transformers import ElectraModel, ElectraTokenizer >>> model = ElectraModel.from_pretrained("monologg/koelectra-base-v2-generator") >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v2-generator") ``` | 5b704dab92ecd3c118a93cd717d76fc6 |
apache-2.0 | ['korean'] | false | Tokenizer example ```python >>> from transformers import ElectraTokenizer >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v2-generator") >>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]") ['[CLS]', '한국어', 'EL', ' | 1be7365ae0f01b9cae617a08c08a2713 |
apache-2.0 | ['korean'] | false | Example using ElectraForMaskedLM ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="monologg/koelectra-base-v2-generator", tokenizer="monologg/koelectra-base-v2-generator" ) print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token))) ``` | 09258bff07932b3e1710e10c79d5c814 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_300k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 300k 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 ... | 584ea701ab6f127a9c55a52b5a00ed7a |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_300k'] | 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_300k') model = TFBertModel.from_pretrained("google/multibe... | e85fb207200a02172752f04edc6e5f92 |
mit | [] | false | tamiyo on Stable Diffusion This is the `<tamiyo>` 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 y... | ced6fe2c80e010294713c6ab1911e277 |
apache-2.0 | ['multiberts', 'multiberts-seed_24'] | false | MultiBERTs - Seed 24 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 variatio... | e6efc60308d1e7f5554ba75423536092 |
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