license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training unicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish. Since training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments: --num_tr... | ce3af9a2f35978a53e0069b995ccbe2a |
apache-2.0 | ['generated_from_keras_callback'] | false | mayank-soni/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0475 - Validation Loss: 3.3455 - Epoch: 7 | 19aa8839e801a4d5a0050db3f638daf7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.8713 | 4.1729 | 0 | | 5.8463 | 3.7092 | 1 | | 5.1036 | 3.5528 | 2 | | 4.7009 | 3.4817 | 3 | | 4.4143 | 3.4132 | 4 | | 4.2395 |... | 2c584ca9fb4804613ebc755561dc989a |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | bert-large-japanese-wikipedia-ud-head-finetuned-squad This model is a fine-tuned version of [KoichiYasuoka/bert-large-japanese-wikipedia-ud-head](https://huggingface.co/KoichiYasuoka/bert-large-japanese-wikipedia-ud-head) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9130 | 7a896dc2b750a9803c4f349491c0af4c |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 50 | 1.9136 | | No log | 2.0 | 100 | 1.9691 | | No log | 3.0 | 150 | 1.9130 | | 1a6536ee59e19a92aafd189ec06c2e7a |
apache-2.0 | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** Varad Bhatnagar, Diptesh Kanojia and Kameswari Chebrolu - **Model type:** Summarization - **Language(s) (NLP):** English - **Finetuned from model:** https://huggingface.co/sshleifer/distilbart-cnn-12-6 | 0607edfa3aef48e76a9ef39595fc7891 |
apache-2.0 | [] | false | Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> Finetuning the pretrained Distilled BART model on the 567 pairs released in our paper. | 0ee69765cf49f37921179f6ff7f9f1f6 |
apache-2.0 | [] | false | How to Get Started with the Model Use the code below to get started with the model. ``` from transformers import BartForConditionalGeneration, BartTokenizerFast hft = BartTokenizerFast.from_pretrained('varadhbhatnagar/fc-claim-det-DBART') hfm = BartForConditionalGeneration.from_pretrained('varadhbhatnagar/fc-claim-d... | 7f065a6255d004b2e51e181044a61d58 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-distilroberta-v1 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. | 55e4381f8b5361d4e052cfb945de4bc9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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 sente... | f1f798492cbb91a7c77c275569fa0fc3 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-distilroberta-v1') model = AutoModel.from_pretrained('sentence-transformers/all-distilroberta-v1') | 82eb8283db3ce723803dbafae2840641 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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/all-distilroberta-v1) ------ | 687ff657a659be6dff7369c9a197dc6b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`distilroberta-base`](https://huggingface.co/distilroberta-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contr... | 5334cf5fea3ba34fd2d64f06eaff7cf2 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Pre-training We use the pretrained [`distilroberta-base`](https://huggingface.co/distilroberta-base). Please refer to the model card for more detailed information about the pre-training procedure. | 658e37525696622f31098606fb34740d |
agpl-3.0 | ['roberta', 'icelandic', 'masked-lm', 'pytorch'] | false | IceBERT-ic3 This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below. | Dataset | Size | Token... | f7d7c5d1a126df7a11b2fc00d9d815df |
cc-by-4.0 | ['yolov5', 'yolo', 'digital humanities', 'object detection', 'computer-vision', 'document layout analysis', 'pytorch'] | false | DataCatalogue (or DataCat) [DataCatalogue](https://github.com/DataCatalogue) is a research project jointly led by Inria, the Bibliothèque nationale de France (National Library of France), and the Institut national d'histoire de l'art (National Institute of Art History). It aims at restructuring OCR-ed auction sale c... | 90c9b90e6e126f8aa08afc7f5c2b5608 |
cc-by-4.0 | ['yolov5', 'yolo', 'digital humanities', 'object detection', 'computer-vision', 'document layout analysis', 'pytorch'] | false | DataCat Yolov5 We trained a YOLOv5 model on custom data to perform document layout analysis on auction sale catalogs. The training set consists of **581 images**, annotated with **two classes**: * *title* (585 instances) * *entry* (it refers to a catalog entry) (5017 instances) 59 images were used for validation. ... | ede91d97828db4ffe1f7581bbe924248 |
cc-by-4.0 | ['yolov5', 'yolo', 'digital humanities', 'object detection', 'computer-vision', 'document layout analysis', 'pytorch'] | false | Demo An interactive demo is available on the following HugginFace Space: https://huggingface.co/spaces/HugoSchtr/DataCat_Yolov5 <img alt='detection example' src="https://huggingface.co/HugoSchtr/yolov5_datacat/resolve/main/eval/detection_example.png" width=30% height=30%> | 3099c1692bb3f9595ce348c3f9aa15bc |
cc-by-4.0 | ['yolov5', 'yolo', 'digital humanities', 'object detection', 'computer-vision', 'document layout analysis', 'pytorch'] | false | What's next The model performs well on our data and now needs to be incorporated into a dedicated pipeline for the research project. We also plan to train a new model on a larger training set in the near future. | 5708e0b73ae7542158c53b6343e697f9 |
mit | [] | false | Floral-orchid on Stable Diffusion This is the `<floral-orchid>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You ca... | 942439536dae06bf0d7f04ae75e959ef |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-PanoAI2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1537 | b67048c14bb37d6fd42e7bb707b274a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 2 | 4.2481 | | No log | 2.0 | 4 | 4.1813 | | No log | 3.0 | 6 | 4.1537 | | 16a735ac1adee3886f6cd5ea1d2046f6 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | SepFormer trained on WHAM! for speech enhancement (16k sampling frequency) This repository provides all the necessary tools to perform speech enhancement (denoising) with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAM!](http://wham.whisper.ai/) dataset wi... | 25d454476f1b052395b9f60108d36096 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | Perform speech enhancement on your own audio file ```python from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-wham16k-enhancement", savedir='pretrained_models/sepformer-wham16k-enhancement') | e9b7ceb105d9b4c2ea9541d5d2bdd839 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-wham16k-enhancement/example_wham16k.wav') torchaudio.save("enhanced_wham16k.wav", est_sources[:, :, 0].detach().cpu(), 16000) ``` | 75ed10207ce12048b105859ffdc5bc73 |
apache-2.0 | ['audio-to-audio', 'Speech Enhancement', 'WHAM!', 'SepFormer', 'Transformer', 'pytorch', 'speechbrain'] | false | Training The training script is currently being worked on an ongoing pull-request. We will update the model card as soon as the PR is merged. You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1bbQvaiN-R79M697NnekA7Rr0jIYtO6e3). | 1582b8cfb23d21b053cd668c23c53182 |
apache-2.0 | ['English to Nepali Translator', 'MT5 Fine Tuned', 'Nepali Translator Dataset'] | false | Model description This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) trained on "Nepali Translator" dataset. This fine-tuned model is for translating English to Nepali language (with limited capability) | 622fd2708eb75d384218ed5b57663376 |
apache-2.0 | ['English to Nepali Translator', 'MT5 Fine Tuned', 'Nepali Translator Dataset'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 97be4a2e2fb17f7477f19aba0a1eb7d9 |
apache-2.0 | ['English to Nepali Translator', 'MT5 Fine Tuned', 'Nepali Translator Dataset'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8424 | 0.26 | 500 | 2.9765 | | 3.2655 | 0.53 | 1000 | 2.5221 | | 2.9425 | 0.79 | 1500 | 2.3160 | | 2.7533 | 1.05 | 2000 | 2.2170 ... | a4825106af341a27733a23b8dc3626a4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-prueba2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.6356 | a9085441b0f743e2be752078e75ec452 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 3.9054 | | No log | 2.0 | 18 | 3.1893 | | No log | 3.0 | 27 | 2.9748 | | No log | 4.0 | 36 | 3.1541 ... | 4dbb3e8210452306f6a66ff213ee2252 |
apache-2.0 | ['generated_from_keras_callback'] | false | AdwayK/hugging_face_biobert_MLMA This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0 - Validation Loss: 0.0814 - Epoch: 9 | 6c698c18a524cbedc62833d7ffe40812 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3390, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | edbeeca8c41cea8361d05b2ff73a7970 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0 | 0.0579 | 0 | | 0.0 | 0.0509 | 1 | | 0.0 | 0.0544 | 2 | | 0.0 | 0.0621 | 3 | | 0.0 | 0.0671 | 4 | | 0.0 |... | 1eb94eb6aa535882795bdb35b6984cb5 |
apache-2.0 | ['generated_from_trainer'] | false | output This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on dataset [x-tech/cantonese-mandarin-translations](https://huggingface.co/datasets/x-tech/cantonese-mandarin-translations). | d0f00190196a53e940560ab87bf80ae5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | ba5ad161b4c81ffdbbb7d3b180372bec |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8574 | cfd1cac67ada2e83b5da33553c742a42 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0916 | 1.0 | 2346 | 7.0492 | | 6.9039 | 2.0 | 4692 | 6.8751 | | 6.8845 | 3.0 | 7038 | 6.8929 | | f29c35a2353ddecc44b80f6313b9fa5c |
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: 0.2156 - Accuracy: 0.92 - F1: 0.9200 | 2ed30caa0eaaffff27d9326c057ea29a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8096 | 1.0 | 250 | 0.3081 | 0.9005 | 0.8974 | | 0.2404 | 2.0 | 500 | 0.2156 | 0.92 | 0.9200 | | 6dfb4ffd7e2ac597a7e8bc90693dd121 |
apache-2.0 | ['generated_from_trainer'] | false | egy-slang-model This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9273 - Wer: 1.0000 | dbb00cf9c56a93c8597b5d98b2e08d7d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.64 | 200 | 2.9735 | 1.0 | | 3.8098 | 3.28 | 400 | 2.9765 | 1.0 | | 3.8098 | 4.91 | 600 | 2.9662 | 1.0 | |... | 071ec8a39a19dd603a54eca3824d4b94 |
apache-2.0 | ['generated_from_trainer'] | false | hubert-base-cc-finetuned-forum This model is a fine-tuned version of [SZTAKI-HLT/hubert-base-cc](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4746 | 4111fba1bf28eb71ad5dfc62aeb7d86b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7966 | 1.0 | 157 | 2.5139 | | 2.6303 | 2.0 | 314 | 2.4601 | | 2.5525 | 3.0 | 471 | 2.4501 | | c3d8ba4cf2106a080181c49812733c47 |
apache-2.0 | ['generated_from_keras_callback'] | false | DLL888/bert-base-uncased-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on [SQuAD](https://huggingface.co/datasets/squad) dataset. It achieves the following results on the evaluation set: - Exact Match: 80.21759697256385 - F1: 87.77849998885436 | 526735b1fcb14dbdda852bbc0cea1929 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training Machine Trained in Google Colab Pro with the following specs: - A100-SXM4-40GB - NVIDIA-SMI 460.32.03 - Driver Version: 460.32.03 - CUDA Version: 11.2 Training took about 26 minutes for two epochs. | 8fda152d0f0d5cc15818b2daf332c693 |
apache-2.0 | ['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': 10564, '... | ea8112fc54d09ad2e9c8fb91efc267fa |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 1ebeb25458b4f6156e5117620d7e85be |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2215 - Accuracy: 0.9343 | b9258861e542598a2ec42c51be9821fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1948 | 1.0 | 1250 | 0.1743 | 0.933 | | 0.0979 | 2.0 | 2500 | 0.2215 | 0.9343 | | fc878abb5e78eea4fd3b720053cc48de |
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_de-it-smt", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict(... | 4a09fb8a5f7f2c703506c4ca2289538c |
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: 0.1583 - F1: 0.8563 | 21efea06b56b6eda6e3543f8fe7def93 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.1748 | 0.8282 | | 0.2366 | 2.0 | 716 | 0.1580 | 0.8434 | | 0.2366 | 3.0 | 1074 | 0.1583 | 0.8563 | ... | d038c86ab407a42e71cd59a8e9ff7bf3 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3175 - Accuracy: 0.8733 - F1: 0.8733 | 296efd03a5027a555f6256075528be04 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_accent_us-8_england-2_s875 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | a2635d30251f83c379e32e240ec8ee06 |
apache-2.0 | ['generated_from_trainer'] | false | apm1 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: - Loss: 0.0001 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 | 0d88ed4ad36a799de32e38f4ff83b959 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 | d32eedab88b3d62511d45ae6be1258cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 157 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 314 | 0... | abacf58eaa06d4b5e31298cc4b315161 |
creativeml-openrail-m | [] | false | <a href="https://colab.research.google.com/drive/1hlSMYEq3pyX-fwTSqIOT1um80kU1yOJF?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> PromptCLUE：全中文任务零样本学习模型 这个模型是PromptCLUE-base-v1-5模型适应PaddleNLP转化得到的。PromptCLUE-base-V1-5是基于PromptCLUE-base进一步训练(+50%步数)，以及更多任务(+50%任务)以及更多任务类型上进行训... | 6b1bbd67990f898a610b893db844412a |
creativeml-openrail-m | [] | false | 加载模型 from paddlenlp.transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("ClueAI/PromptCLUE-base-v1-5", from_hf_hub=False) model = T5ForConditionalGeneration.from_pretrained("ClueAI/PromptCLUE-base-v1-5", from_hf_hub=False) ``` 使用模型进行预测推理方法： ```python import torch | db47a20d5754d6230bca8c6c37c6e4ae |
creativeml-openrail-m | [] | false | 这里使用paddle的gpu版本,推理更快 def preprocess(text): return text.replace("\n", "_") def postprocess(text): return text.replace("_", "\n") def answer(text, sample=False, top_p=0.8): '''sample：是否抽样。生成任务，可以设置为True; top_p：0-1之间，生成的内容越多样''' text = preprocess(text) encoding = tokenizer(text=[text], truncation=True, padd... | 3c51ffeb2442ddfef4c56088cce37725 |
mit | [] | false | Stretch RE1 Robot on Stable Diffusion This is the `<stretch>` 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 ... | cf50990196d55aacc957a307bd71276e |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-ours-run-1 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3970 - Accuracy: 0.735 - Precision: 0.7033 - Recall: 0.6790 - F1: 0.6873 | 199a97baef4bca5e0cd77523d3175827 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9719 | 1.0 | 200 | 0.8460 | 0.635 | 0.6534 | 0.5920 | 0.5547 | | 0.7793 | 2.0 |... | c3ca24fb22a6274bc622e36c5ea4179c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6169 - Matthews Correlation: 0.5528 | c00aa80141d81a988df6dce9d4d60752 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5233 | 1.0 | 535 | 0.5188 | 0.4126 | | 0.3459 | 2.0 | 1070 | 0.5068 | 0.4955 | | 0.2... | b16a3ea08c136e9f02cc60f30e7d2d04 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_43 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4611 - Accuracy: 0.8374 - Macro-f1: 0.8364 - Weighted-macro-f1:... | 695000cf433bcf880414026e0198f0b5 |
apache-2.0 | ['generated_from_trainer'] | false | hate_trained_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8996 - F1: 0.7665 | 07e13fdad386cbc65733f6c98e3378e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 05b1dada04823ad1333419aaafae9ef0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4833 | 1.0 | 563 | 0.4834 | 0.7543 | | 0.3275 | 2.0 | 1126 | 0.5334 | 0.7755 | | 0.2111 | 3.0 | 1689 | 0.6894 | 0.7674 | |... | 76c48f27cb9f21fea2afcfea01f6b9f0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0789 - Matthews Correlation: 0.5222 | 313c88d3671d272c2401dee90f269576 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.1472 | 1.0 | 535 | 0.8407 | 0.4915 | | 0.1365 | 2.0 | 1070 | 0.9236 | 0.4990 | | 0.1... | 44e4ed2949051e5be22e39d3070e88be |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_qnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6494 - Accuracy: 0.6136 | 0cc6be25c1b1a2aea5d9e359d3c8d656 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6774 | 1.0 | 819 | 0.6494 | 0.6136 | | 0.6378 | 2.0 | 1638 | 0.6508 | 0.6055 | | 0.6148 | 3.0 | 2457 | 0.6578 | 0.... | 6357c4dca809096d6cd52191dba6ad80 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb-whole-word-masking 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: 3.3043 | 9e7e95e760bdc31ba0c2a1e27b3df7f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5536 | 1.0 | 157 | 3.3242 | | 3.4026 | 2.0 | 314 | 3.2848 | | 3.3708 | 3.0 | 471 | 3.2791 | | a0dc8dcbcf405902d4476d48f3ff8a68 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'rust'] | false | This repository hosts weights for a Rust based version of Stable Diffusion. These weights have been directly adapted from the [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) weights, they can be used with the [diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) crat... | ad9f5d0fe3aa1309860e82679fd443ac |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'rust'] | false | License The license is unchanged, see the [original version](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL). In line with paragraph 4, the original copyright is preserved: Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors The model details section below is copied from... | 6605e0999a7e6c9432d83d811c4c5a6e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'rust'] | false | Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a ... | 1e30c924c58d3f6cb1c9cbfa5b296800 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'rust'] | false | Weight Extraction The weights have been converted by downloading them from the stabilityai/stable-diffusion-2-1 repo, and then running the following commands in the [diffusers-rs repo](https://github.com/LaurentMazare/diffusers-rs). After downloading the files, use Python to convert them to `npz` files. ```python i... | 93d58eb3daae8a3b709757be2ce8246c |
apache-2.0 | ['generated_from_trainer'] | false | vc-bantai-vit-withoutAMBI-adunest This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1950 - Accuracy: 0.9389 | e7a10d670917bb7022905d0246aa6730 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | b0f4d6a02a8f9b4a47163431cb6dcd50 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4821 | 0.11 | 100 | 0.7644 | 0.6714 | | 0.7032 | 0.23 | 200 | 0.5568 | 0.75 | | 0.5262 | 0.34 | 300 | 0.4440 | 0.... | a186173482e261ccbb54dce6788ed281 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_unispeech_s714 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 883697cb4bed7a6806e8b8cd37705d2c |
apache-2.0 | ['translation'] | false | bul-ukr * source group: Bulgarian * target group: Ukrainian * OPUS readme: [bul-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md) * model: transformer-align * source language(s): bul * target language(s): ukr * model: transformer-align * pre-processing: normalization + ... | c1d5dea5d7b1529d3ea985e12b6f4976 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bul-ukr - source_languages: bul - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['bg', 'uk'] - src_constituents: {'bul', 'bul_Latn'} ... | 050cc6ceeb44346497dcdcf90eb749e6 |
mit | ['spacy', 'token-classification'] | false | de_dep_news_trf German transformer pipeline (bert-base-german-cased). Components: transformer, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer). | Feature | Description | | --- | --- | | **Name** | `de_dep_news_trf` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | ... | a2e5a452cb45586fb6586de852dd04d0 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (766 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `NNE`, `PDAT`, `PDS`, `P... | 71543026519bb969e3c708a4a8813850 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.96 | | `TOKEN_P` | 99.92 | | `TOKEN_R` | 99.90 | | `TOKEN_F` | 99.91 | | `TAG_ACC` | 99.06 | | `POS_ACC` | 99.15 | | `MORPH_ACC` | 97.00 | | `MORPH_MICRO_P` | 98.83 | | `MORPH_MICRO_R` | 98.87 | | `MORPH_MICRO_F` | 98.85 | | `SENTS_P` | 98.26 | | `SENTS_R` | ... | 3bf9790cdb321ecd6c1bb852a27905a8 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-biomedical-clinical-es-finetuned-ner This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1382 | 687d95a444a6fff8f49a2763a7913166 |
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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 14 | 40409d3665da00508e9f8e76edf2e96d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3315 | 1.0 | 12 | 0.3878 | | 0.2419 | 2.0 | 24 | 0.2655 | | 0.175 | 3.0 | 36 | 0.1888 | | 0.1441 | 4.0 | 48 | 0.1808 ... | e7bd899b72277b02eed1cfd742aeb43a |
creativeml-openrail-m | [] | false | Description Elynia Diffusion is a latent text-to-image diffusion model based on the original CompVis Stable Diffusion v1.4 and then fine-tuned on the main character of 'Battle for Wesnoth' add-ons using Dreambooth. This model has been created to explore the possibilities and limitations of Dreambooth training and to ... | b3496900f68a8fe6ae1f78b0c47ff6f9 |
creativeml-openrail-m | [] | false | Model Description The model originally used for fine-tuning is Stable Diffusion V1-4, which is a latent image diffusion model trained on LAION2B-en. The current model has been fine-tuned with a learning rate of 5.0e-6 for 800 steps using Dreambooth on character portraits and pixel-art videogame sprites. | cd75abe260d1bc0a4d9ab95fdf4d1f69 |
creativeml-openrail-m | [] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outp... | f42e14bb1bd9d5daff189b1d481ab0e8 |
creativeml-openrail-m | [] | false | Acknowledgements This project would not have been possible without the incredible work by the CompVis Researchers, Wesnoth devs, artists and user made content makers. The dataset for training currently resides here https://drive.google.com/drive/folders/1gskg6q8s-VWLlav-eVkkAzFP6xiVjY8U?usp=sharing. TODO: make a p... | 5c1ccd806a2ccc15d07726207c4cc9d3 |
creativeml-openrail-m | [] | false | Preview Images https://imgur.com/a/CnIPfrQ IMPORTANT INSTRUCTIONS! This model was trained on SD base 1.5 version BUT It does also work for 1.4 as they both share the same Clip encoder. Install instructions. Simply place the invisible.pt file inside the \stable-diffusion-webui\models\hypernetworks folder. Load the ... | 4bd5eb923ea4ed6d91872cfc4dae78a5 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-es_s26 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 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 8fe0f7209ea788dbd1c985a1fb53c374 |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Multi QA MPNet base model for Semantic Search This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. This model uses [`m... | 47b6b7f723ca07180a51fc27e6bbd3fe |
mit | ['feature-extraction', 'sentence-similarity', 'sentence-transformers'] | false | Training Data We use the concatenation from multiple datasets to fine-tune this model. In total we have about 215M (question, answer) pairs. The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html | b4ff78710fdc92d5d9d1ba073b93fba6 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.