license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | Collage cutouts on Stable Diffusion This is the `<collage-cutouts>` 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. Yo... | ca6601f903b17c298decb361639d8898 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the lvngrooms concept trained by lakssrini on the custom real estate listings dataset. This is a Stable Diffusion inpainting model fine-tuned on the lvngrooms concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of lvngrooms room** This model was created as part ... | bc0be262a64411e1b573ed75a62a4aee |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-emotion-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1496 - Accuracy: 0.9435 | 991ed32298a5808f1d57c9a58c54dcc3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4501 | 1.0 | 1000 | 0.2432 | 0.924 | | 0.1947 | 2.0 | 2000 | 0.1646 | 0.934 | | 0.1497 | 3.0 | 3000 | 0.1382 | 0.... | fd0f2b9cfd9062748c84f509ee9a6ce4 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Multi-band MelGAN trained on Thorsten (Ger) This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on Thorsten dataset (ger). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). | 5ab1940e1a3ec4579e1247451cf9d9fa |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Converting your Text to Wav ```python import soundfile as sf import numpy as np import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-thorsten-ger") tacotron2 = TFAutoModel.fro... | a6909ed790bb37f38161df7841fa1959 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | About the model The model has been trained on a dataset containing [249525 sentences with US English spelling](https://www.englishvoice.ai/p/us-to-uk/ "249525 sentences with US English spelling"), along with their UK English equivalent. The purpose of the model is to rewrite sentences from US English to UK English... | 0e917237edbef6b6558620ae0fa014f7 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Generation examples | Input | Output | | :------------ | :------------ | | My favorite color is yellow. | My favourite colour is yellow. | | I saw a guy in yellow sneakers at the subway station. | I saw a bloke in yellow trainers at the underground station. | | You could have gotten hurt! | You could have got hurt! |... | 2a317651926ac71b72604370319e2459 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Sample code Sample Python code: ```python import torch from transformers import T5ForConditionalGeneration,T5Tokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-us-to-uk-english") tokenizer = T5Tokenizer.from_pretr... | 70c37512c72baa5b939167ab4aeddfe1 |
mit | ['vision', 'video-classification'] | false | X-CLIP (large-sized model) X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on [Kinetics-600](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et... | 171c3715a85b74d8194dd6c54c413739 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_datetime-roberta-large-v1-2-95 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 Transfor... | 5940df02edfdcef8dbd6dd7c6b6ba3f8 |
apache-2.0 | ['translation'] | false | opus-mt-es-gil * source languages: es * target languages: gil * OPUS readme: [es-gil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-gil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 96069b633f41e5f6870eb157e92b631d |
apache-2.0 | ['translation'] | false | opus-mt-en-ho * source languages: en * target languages: ho * OPUS readme: [en-ho](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ho/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 5e04961ffde9fa2386ca1d7c8e6ca7a9 |
apache-2.0 | ['generated_from_trainer'] | false | bert-hateful-memes-expanded This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on texts from the following datasets: - [Hateful Memes](https://hatefulmemeschallenge.com/), `train`, `dev_seen` and `dev_unseen` - [HarMeme](https://github.com/di-dimitrov/harmeme), `train`... | a00d6126720fc7a7e0bd639a5e450a09 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 69b18052cd3da5aaecce6cc9bf3ce4af |
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: 0.7470 - Matthews Correlation: 0.5414 | 448dcfbf28c7b8abb2681167201e534f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5237 | 1.0 | 535 | 0.5327 | 0.4248 | | 0.347 | 2.0 | 1070 | 0.5105 | 0.5239 | | 0.2... | ff4026a8b6096463d7acf2838af24e06 |
cc-by-4.0 | ['anglicisms', 'loanwords', 'borrowing', 'codeswitching', 'flair', 'token-classification', 'sequence-tagger-model', 'arxiv:2203.16169'] | false | anglicisms-spanish-flair-cs This is a pretrained model for detecting unassimilated English lexical borrowings (a.k.a. anglicisms) on Spanish newswire. This model labels words of foreign origin (fundamentally from English) used in Spanish language, words such as *fake news*, *machine learning*, *smartwatch*, *influenc... | 904204960aff64b70062fe84520c9c5e |
cc-by-4.0 | ['anglicisms', 'loanwords', 'borrowing', 'codeswitching', 'flair', 'token-classification', 'sequence-tagger-model', 'arxiv:2203.16169'] | false | Metrics (on the test set) Results obtained on the test set of the [COALAS](https://github.com/lirondos/coalas/) corpus. | LABEL | Precision | Recall | F1 | |:-------|-----:|-----:|---------:| | ALL | 90.14 | 81.79 | 85.76 | | ENG | 90.16 | 84.34 | 87.16 | | OTHER | 85.71 | 13.04 ... | f1d4f5c55ce5d7299c4a3607ca7965db |
cc-by-4.0 | ['anglicisms', 'loanwords', 'borrowing', 'codeswitching', 'flair', 'token-classification', 'sequence-tagger-model', 'arxiv:2203.16169'] | false | Minor patch needed if you are running from Windows temp = pathlib.PosixPath pathlib.PosixPath = pathlib.WindowsPath tagger = SequenceTagger.load("lirondos/anglicisms-spanish-flair-cs") text = "Las fake news sobre la celebrity se reprodujeron por los mass media en prime time." sentence = Sentence(text) | e3de45f4390101e36fbb98c9ceb72a66 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-intent-classification-ori-f1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4353 - F1: 0.875 | e689aeffca571e0312af3e7f81bff8f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - 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 - lr_schedu... | f7f36c77b6f037b51c019cd757a01be8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.19 | 1.0 | 28 | 2.1733 | 0.2708 | | 2.1205 | 2.0 | 56 | 2.1125 | 0.2708 | | 2.0965 | 3.0 | 84 | 2.0543 | 0.2708 | |... | 6386c9e31afa14a530122b04e1a401a6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_mnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5196 - Accuracy: 0.6066 | 1ae32bf1f46afcb1ac0f3b466b1c1431 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5795 | 1.0 | 1534 | 0.5536 | 0.5297 | | 0.5482 | 2.0 | 3068 | 0.5402 | 0.5549 | | 0.5312 | 3.0 | 4602 | 0.5303 ... | 0f8c0addfab9a751d1f8b6ba700e3250 |
apache-2.0 | ['generated_from_keras_callback'] | false | lyhhhhhh/mt5-small-finetuned-test 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: 2.2262 - Validation Loss: 1.8557 - Epoch: 7 | ef5ec2e16ca37f0c5c77bd90e8e1dff6 |
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': 5.6e-05, 'decay_steps': 64112, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | d47e75cb7bae46e3dcd62e0fc9f1af68 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.0384 | 2.3228 | 0 | | 2.7913 | 2.1021 | 1 | | 2.5264 | 1.9837 | 2 | | 2.4013 | 1.9247 | 3 | | 2.3268 | 1.8783 | 4 | | 2.2781 |... | 960b3352321906841e303ac7fb0a970e |
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", "as", split="test[:2%]") processor = Wav2Vec2Processor.from_pretr... | 8e9a3db49d2bf6c4780b2bf71e333731 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Assamese 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", "as", split="test") wer = ... | 33e520fea6377d69400cf8fb5a864a12 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-misogyny-sexism-multilingual This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2382 - Accuracy: 0.843... | 7295027118617ec563cac4ac5da37f88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3663 | 1.0 | 2062 | 0.3696 | 0.8363 | 0.7605 | 0.7967 | 0.7274 | 0.16... | 21dca610b75bf0a2abec57d8a4b2e4d0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8072 - Wer: 0.6531 | 9d9ce1cb2dec26cd94c936bad6d08770 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 11.0967 | 1.0 | 118 | 4.6437 | 1.0 | | 3.4973 | 2.0 | 236 | 3.2588 | 1.0 | | 3.1305 | 3.0 | 354 | 2.6566 | 1.0 | |... | 79047b4533c2803ecb7d818feaca36b6 |
openrail++ | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Future Diffusion This is the fine-tuned Stable Diffusion 2.0 model trained on high quality 3D images with a 768x768 pixel resolution. Use the tokens `redshift style` in your prompts for the effect. Trained on Stability.ai's [Stable Diffusion 2.0](https://huggingface.co/stabilityai/stable-diffusion-2) with 768... | 7edd146eb02fe779c4e35d3abc02a99f |
openrail++ | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Prompt and settings for the Characters: **redshift style portrait black female cyberpunk hacker tattoos colorful short hair wearing a crop top redshift style Negative Prompt: mutated body double head bad anatomy long face long neck long body text watermark signature** _Steps: 20, Sampler: Euler a, CFG scale: 7, Si... | a82366370ede913280b2336fcd6f6cb4 |
openrail++ | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Prompt and settings for the Landscapes: **redshift style beautiful fjord at sunrise Negative Prompt: fog blurry soft** _Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 1536x768_ This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text... | bbb86d7a52ac80fa397f7e056f558ef0 |
cc-by-sa-4.0 | ['Summarization', 'abstractive summarization', 'mbart-large-cc25', 'Czech', 'text2text generation', 'text generation'] | false | mbart25-multilingual-summarization-multilarge-cs This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Multilingual large summarization dataset focused on Czech texts to produce multilingual summaries. | 48ff1e09c0132d7a050fa4e8d9f56e58 |
cc-by-sa-4.0 | ['Summarization', 'abstractive summarization', 'mbart-large-cc25', 'Czech', 'text2text generation', 'text generation'] | false | Task The model deals with a multi-sentence summary in eight different languages. With the idea of adding other foreign language documents, and by having a considerable amount of Czech documents, we aimed to improve model summarization in the Czech language. Supported languages: 'en_XX' : 'en', 'de_DE': 'de', 'es_XX': ... | bac158f6ff977db876413ef49bc2936d |
cc-by-sa-4.0 | ['Summarization', 'abstractive summarization', 'mbart-large-cc25', 'Czech', 'text2text generation', 'text generation'] | false | ROUGE results per individual dataset test set: | ROUGE | ROUGE-1 | | | ROUGE-2 | | | ROUGE-L | | | |-----------|---------|---------|-----------|--------|--------|-----------|--------|--------|---------| | dataset |Precision | Recall | Fscore | Precision | Recall | Fscore | Precision | Recall | Fsco... | d0fcf71202e94ab35145ba2351463c0f |
apache-2.0 | [] | false | Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This is a Flan-T5-Base model finetuned for different safety tasks. This model is planned to be used in Open Assistant, an Open Source chatGPT alternative. | b3091b11b92bdeb87aa41599e4e8ad21 |
apache-2.0 | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** SummerSigh - **Model type:** Flan-t5 - **Language(s) (NLP):** English - **License:** Apache-2.0 - **Finetuned from model [optional]:** flan-t5-base | 8f90f8d5588aef0009147bd858003ae7 |
apache-2.0 | [] | false | Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Used to clean toxic language, summarize user prompts into small chat headers, and generate Rules of Thumb. | 8269bdb41f8c89740f3c1a0a053944e4 |
apache-2.0 | [] | false | Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> "ST_SAFE_CHECK: " checks if the prompt is safe, if not safe, explain why. "ST_PROMPT: " takes a prompt and summarizes the request. "ST_REPHRASE: " takes a prompt that has toxic language and makes it ... | 230a73eb4e3cb287a15d88e16e432f31 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-large-bne-finetuned-go_emotions-es This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on the go_emotions dataset. It achieves the following results on the evaluation set: - Loss: 3.2457 - Accuracy: 0.5668 - F1: 0.5572 | 98c73cfc16dde1060640a9a8d3d704dc |
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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 84f6459592703489200cc47ab84e9c51 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 1.5678 | 1.0 | 9077 | 1.5649 | 0.5671 | 0.5197 | | 1.3898 | 2.0 | 18154 | 1.5005 | 0.5776 | 0.5492 | | 0.915 ... | 0c4310a5c699e054286c8d45fcc7c6f3 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic', 'onnx'] | false | PyTorch This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [electra-small-discriminator-mrpc](http... | 0f6fb735bb758c4ff43ebb34c2c3af5f |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic', 'onnx'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/electra-small-discriminator-mrpc-int8-static', ) ``` | f3e136f86f09bb3cbbd552181305ead5 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [electra-small-discriminator-mrpc](https://huggingface.co/Intel/electra-small-discriminator-mrpc). The calibration dataloader is the eval dat... | a8ba6c7287129ba8a694dd5ff5e03ee0 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingStatic', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/electra-small-discriminator-mrpc-int8-static') ``` | fbc5d275965cfeddff40433cd1684c52 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-ascend This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ascend dataset. It achieves the following results on the evaluation set: - Loss: 4.9059 - Wer: 0.6633 | 5a550392aff8d1a7eb3e539a6ce7e2ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 22.8456 | 1.32 | 400 | 5.4265 | 1.0 | | 5.1649 | 2.65 | 800 | 5.0265 | 1.0 | | 4.8311 | 3.97 | 1200 | 4.7929 | 1.0 | |... | 73f19f521db4164840bf7c61e8523fb6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-uncased-sep-26 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0483 - F1: 0.9369 | d8ada9c366c5f294a2e14b91cee00f7c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 3 | 8ef66624357ba59ad66d46c4901dca69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0798 | 1.0 | 8623 | 0.0682 | 0.8979 | | 0.0498 | 2.0 | 17246 | 0.0551 | 0.9270 | | 0.0351 | 3.0 | 25869 | 0.0483 | 0.936... | b9dc7621bcd8acd93eaec4bc5d772a3e |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-news This model is a fine-tuned version of [jestemleon/bert-nlp-project-news](https://huggingface.co/jestemleon/bert-nlp-project-news) on the [news](https://huggingface.co/datasets/steciuk/news) dataset. It achieves the following results on the evaluation set: - Loss: 0.4362 - Accuracy: 0.9078 - F... | c94297143a2ffb24794d2a7aca70b907 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4106 | 0.37 | 120 | 0.3049 | 0.8797 | 0.8444 | | 0.3078 | 0.75 | 240 | 0.3038 | 0.8891 | 0.8683 | | 0.2642 |... | e0b5994044bd61594fa7422f122ed58c |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Small Cased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pretra... | 2ec15ef1cea77ebd9742c60ae23f17f7 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1798 - Accuracy: 0.0845 | a48d74802fbaf36a22363f34458af3a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6896 | 25.0 | 500 | 0.7651 | 0.2535 | | 0.6597 | 50.0 | 1000 | 1.1537 | 0.1408 | | 0.6018 | 75.0 | 1500 | 1.6711 | 0.... | e98940fae280b114fbc325c165206410 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | article2KW_test2.0c_barthez-orangesum-title_finetuned_for_mlm_77153 This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0474 | 071beb85fb96a2139735d66772c2ac96 |
apache-2.0 | ['mlm', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4053 | 1.0 | 82 | 0.2412 | | 0.2734 | 2.0 | 164 | 0.0641 | | 0.0771 | 3.0 | 246 | 0.0506 | | 0.0601 | 4.0 | 328 | 0.0474 ... | e10d8a2ecfc6767234342fc8026a049b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | 4cc68c46ad83eeb32a1dac3db9269032 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout b0ff60946ada6753af79423a2e6063984bec2926 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp ``` | f7e1930fcdfada21f4a104842eb25adb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 5.0244 | 703e3f9c482ff6990806baf02728fd6a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 60 | 5093bfcdb0ec2d48bf808015610101cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 30 | 3.5643 | | No log | 2.0 | 60 | 2.4546 | | No log | 3.0 | 90 | 2.3018 | | No log | 4.0 | 120 | 2.4636 ... | 2e1abd3728067bb0e2e39f982f36bca3 |
cc-by-4.0 | [] | false | UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-2 We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification. This model is part of a series of models presented at the [TSAR-2022 Shared Task](htt... | 73e244b23b277f5ccd803f30d53bb6f1 |
cc-by-4.0 | [] | false | Models Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold): | Model Name | Run | 64135a175dac9f8c71f18d2639eec9ca |
cc-by-4.0 | [] | false | | Language | Setting | |----------------------------------------------------------------------|----|:-----------:|-----------| | [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune | | [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 ... | 3e116536d4d28e8b56ef75c19f0ce2ab |
cc-by-4.0 | [] | false | Results We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.... | 92b9ef5af5b5262829c3afc7860d93a2 |
cc-by-4.0 | [] | false | | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 | |------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------| | English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** | | Eng... | 6dbe07f0d70d4659713885dc8232c6eb |
cc-by-4.0 | [] | false | Citation If you use our results and scripts in your research, please cite our work: "[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-prompt-ls, ... | e9e017dacc6a653c3552c6e13ab0d059 |
mit | ['gan', 'sngan', 'huggan', 'unconditional-image-generation'] | false | Generated samples <img src="https://raw.githubusercontent.com/ChainYo/docugan/master/documents_samples.png" width="400" height="1200"> Project repository: [DocuGAN](https://github.com/ChainYo/docugan). | 6235700c6747d4a2c53d2db5baf69d08 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | arisha_hatch_session Dreambooth model trained by igorshmel 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/fa... | 5bce69d507abf3476db34168f828679a |
apache-2.0 | ['translation'] | false | spa-run * source group: Spanish * target group: Rundi * OPUS readme: [spa-run](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-run/README.md) * model: transformer-align * source language(s): spa * target language(s): run * model: transformer-align * pre-processing: normalization + Senten... | 217af63914ac00ab41a7a5ed99702990 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-run - source_languages: spa - target_languages: run - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-run/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'rn'] - src_constituents: {'spa'} - tgt_const... | d9b7a0d5ecb085a730ab43ad26cc967c |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 1800k (uncased) Seed 2 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | de083f1230f6c6d032e3399feaa2dcd2 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-1800k') model = BertModel.from_pretrained("multiberts-seed-2-1800k") text = "Replace me by any text you'd lik... | 7ace504ebbb9b57291df0d0fdd82d748 |
apache-2.0 | ['translation'] | false | rus-slv * source group: Russian * target group: Slovenian * OPUS readme: [rus-slv](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-slv/README.md) * model: transformer-align * source language(s): rus * target language(s): slv * model: transformer-align * pre-processing: normalization + Se... | dd783fe861fece394446bf85988a6d05 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-slv - source_languages: rus - target_languages: slv - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-slv/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'sl'] - src_constituents: {'rus'} - tgt_const... | 04f40d5afc9d205f053ebaccbe821248 |
apache-2.0 | ['generated_from_trainer'] | false | dat259-wav2vec2-en2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_1_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.4036 - Wer: 0.5090 | e9e2ab0ff763c5d9e830ebfebb5839b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 59c1e700c1c0d6b6f1d2e63049d759a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.4355 | 1.82 | 200 | 3.0307 | 1.0 | | 2.1744 | 3.64 | 400 | 1.5661 | 0.7449 | | 0.5535 | 5.45 | 600 | 1.3005 | 0.5914 | |... | 63099bb1a7e168e77a08d0bf43baf1f4 |
apache-2.0 | ['masked-auto-encoding', 'generated_from_trainer'] | false | test_mae_flysheet This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/flysheet dataset. It achieves the following results on the evaluation set: - Loss: 0.2675 | f1a0dc8d29b9dd041d666d272c1022d5 |
apache-2.0 | ['masked-auto-encoding', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.75e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100.0 - mixed_pre... | 2c9a15172ccacfcf14f70b5cd0b8c395 |
apache-2.0 | ['masked-auto-encoding', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.284 | 1.0 | 28 | 2.2812 | | 2.137 | 2.0 | 56 | 2.0288 | | 1.6016 | 3.0 | 84 | 1.2437 | | 0.8055 | 4.0 | 112 | 0.7419 ... | 658a11fcaa9358fc44af3c193b4e31d0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0746 - Precision: 0.9347 - Recall: 0.9426 - F1: 0.9386 - Accuracy: 0.9851 | 4684adcb9c2d9ce903d8724c362036ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0832 | 1.0 | 3511 | 0.0701 | 0.9317 | 0.9249 | 0.9283 | 0.9827 | | 0.0384 | 2.0 ... | 71e66def3c2aac4062653dac6cba61d9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5578 | 1.0 | 2249 | 6.4697 | | 6.1907 | 2.0 | 4498 | 6.1998 | | 6.0152 | 3.0 | 6747 | 6.1100 | | aafcb745567e6d91212bf047013efe4f |
mit | [] | false | model by ChicoTofu This your the Stable Diffusion model fine-tuned the Chictof concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks chicto_tstp** You can also train your own concepts and upload them to the library by using [this notebook](https://cola... | db6fa72bf45b5aa98e6bde9f3822ed9f |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Swedish-Talbanken | Feature | Description | | --- | --- | | **Name** | `sv_udv25_swedishtalbanken_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | 2eeccfe5a187ef57c1c6c79dd7b091f9 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1206 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|... | 2bb6d735244b2c231f0e65ce0b6dbc34 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.95 | | `TOKEN_P` | 99.95 | | `TOKEN_R` | 99.96 | | `TOKEN_ACC` | 99.99 | | `SENTS_F` | 98.02 | | `SENTS_P` | 98.02 | | `SENTS_R` | 98.02 | | `TAG_ACC` | 97.87 | | `POS_ACC` | 98.83 | | `MORPH_ACC` | 97.97 | | `DEP_UAS` | 92.14 | | `DEP_LAS` | 89.39 | | `LEMMA_A... | 72d2d2076aa65c8e07e4b1c45654177f |
apache-2.0 | ['generated_from_keras_callback'] | false | datauma/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7773 - Epoch: 1 | d30183015dd9473df22ce70f8b9fb71c |
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': 11090, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 14cafee4fdf03bf891f0a79b051fa4a8 |
mit | [] | false | one line drawing on Stable Diffusion This is the `<lineart>` 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... | 3c6bac09465b1f181b40b58b779d6e21 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-model-imdb-small-3000-demo 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: 0.3310 - Accuracy: 0.8567 - F1: 0.8571 | 8b28f3950c1197a46cf7e15542441992 |
apache-2.0 | [] | false | distilbert-base-nl-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | d04d22432dc03892c03466b81267e793 |
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