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 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_wav2vec2_s211 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 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 55dd1c3e517604f099201101a72b199e |
mit | [] | false | sanguo-guanyu on Stable Diffusion This is the `<sanguo-guanyu>` 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... | 5663b52cea9874c7e512a080bb683a7b |
apache-2.0 | ['generated_from_trainer'] | false | reviews-generator This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 3.4989 | 02362bdd3741046a6ca0bb25e727641d |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 | 4a25ae4b0589765fc6ca0c4b1ee23904 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7955 | 0.08 | 500 | 3.5578 | | 3.7486 | 0.16 | 1000 | 3.4989 | | 9728187c31dd6a06449053126193df5b |
apache-2.0 | ['generated_from_keras_callback'] | false | dpovedano/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0285 - Validation Loss: 0.0612 - Train Precision: 0.9222 - Tra... | 93d12da9d3a2610dcd1709a76f7ffd8e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.0289 | 0.0612 | 0.9222 | 0.9358 | 0.9289 | 0.9834 | 0 ... | e5b49597ac61e16ef84a61f6c3da9451 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Marathi (mr) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](htt... | f625e922453513ac2bed7ea55562b62d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0835 | 4bfb903256cae5271dc00292d57fa7fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0302 | 1.0 | 5546 | 1.0068 | | 0.7597 | 2.0 | 11092 | 0.9976 | | 0.5483 | 3.0 | 16638 | 1.0835 | | 13779834ba8569ad0fa824bd25a24692 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | tiny Turkish Whisper (tTW) This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Ermetal Meetings dataset. It achieves the following results on the evaluation set: - Loss: 6.0735 - Wer: 1.4939 - Cer: 1.0558 | 9aa10f79963b3b80090214b48daf20b7 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 9b05f6fb9b827b57c0282c9deeb19a0f |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-1b-Swedish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: **Without LM** - Loss: 0.3370 - Wer: 18.44 - Cer: 5.75 **With LM** - Loss: 0.33... | 46372a46a88a9b2121ecdf77a43601c8 |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bas... | 1cb5c33c5baea667057ae5615823860f |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sv-SE", split="test", str... | 57664a5e9f28de53365fc7d07b1a278b |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | a558a4ae123387bbba0747e2dc9dc0ad |
apache-2.0 | ['automatic-speech-recognition', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.1562 | 11.11 | 500 | 0.4830 | 0.3729 | 0.1169 | | 0.5655 | 22.22 | 1000 | 0.3553 | 0.2381 | 0.0743 | | 0.3376 | 33.33 |... | 328048c7f2f60cb7797f6a7e650a5005 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-gn-cv8-3 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: 0.9517 - Wer: 0.8542 | 4b23610c804ed32aa9b5ecc4a922d9c2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 19.9125 | 5.54 | 100 | 5.4279 | 1.0 | | 3.8031 | 11.11 | 200 | 3.3070 | 1.0 | | 3.3783 | 16.65 | 300 | 3.2450 | 1.0 ... | 30629e6ea463fa8569740ab8ed85b1ea |
mit | ['generated_from_trainer'] | false | 3label_model This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3920 - Accuracy: 0.8520 | ef891da8a9b1fdaa526ab4b6d17cc40f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6073 | 1.0 | 707 | 0.3921 | 0.8343 | | 0.3319 | 2.0 | 1414 | 0.3920 | 0.8520 | | 972ad3ab352f3e70453e697c8ac85bff |
mit | [] | false | XLNet (base-sized model) XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/). Disclaimer: ... | 1be752f87d86d9cebba75a22c99a2aba |
mit | [] | false | Usage Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import XLNetTokenizer, XLNetModel tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') model = XLNetModel.from_pretrained('xlnet-base-cased') inputs = tokenizer("Hello, my dog is cute", return_... | 4e08c1bf05a632c551fd630cfd8a4a34 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Tacotron 2 with Guided Attention trained on LJSpeech (En) This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https... | 23ef8880c62d56532fea6fa08af94274 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en") taco... | 264690a85221235a46e0c58ac2310f7a |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-NL32 (Deep-Narrow version) T5-Efficient-TINY-NL32 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... | 0f68bc1c6ab21df42a7197578eed5309 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-nl32** - is of model type **Tiny** with the following variations: - **nl** is **32** It has **67.06** million parameters and thus requires *ca.* **268.25 MB** of memory in full precision (*fp32*) or **134.12 MB** of memory in half precision (*f... | d0cce9c4c67893873f75e7aae3d8f9c9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3912 - Accuracy: 0.5881 | 122da806d9709f401497e4c13ec86684 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4059 | 1.0 | 410 | 0.3930 | 0.5733 | | 0.3918 | 2.0 | 820 | 0.3919 | 0.5839 | | 0.3807 | 3.0 | 1230 | 0.3912 | 0.... | e4cf2d2f26a95e91b1eab42a682dc209 |
mit | [] | false | xbh on Stable Diffusion This is the `<xbh>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your ow... | 483623cd57327eeda524db5a67125b65 |
apache-2.0 | [] | false | RuLeanALBERT is a pretrained masked language model for the Russian language using a memory-efficient architecture. Read more about the model in [this blog post](https://habr.com/ru/company/yandex/blog/688234/) (in Russian). See its implementation, as well as the pretraining and finetuning code, at [https://github.co... | 6228b7a60e01f5a5af081c26c81f286f |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1193 - Precision: 0.8333 - Recall: 0.9322 - F1: 0.8800 - Accuracy: 0.9725 | bb0d38c0e36e8604bc0261e9750ed069 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 18 | 0.1216 | 0.8594 | 0.9322 | 0.8943 | 0.9740 | | No log | 2.0 |... | c2d835ae8d0bde9f47df09ec8980bc3d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Archer Diffusion This is the fine-tuned Stable Diffusion model trained on screenshots from the TV-show Archer. Use the tokens **_archer style_** in your prompts for the effect. **If you enjoy my work, please consider supporting me** [](https://pa... | d72ed469995726d96e28cb57c128e695 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/archer-diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a magical princess with golden hair, archer style" im... | 1b43c40958798dffa1876c7f0fb8b1ee |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Prompt and settings for landscapes: **archer style suburban street night blue indoor lighting Negative prompt: grey cars** _Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2915669764, Size: 1024x576_ This model was trained using the diffusers based dreambooth training and prior-preservation loss in 4.000 steps and u... | 110b2324c5c3d743bf80954b921c678c |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | Intended uses and limitations The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. | 44226a440c5af54b8d8586967037efe8 |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | Tokenization and model pretraining This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a **biomedical** corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of [Byte-Pair Encoding... | d7696193d2c96ae1ec140df90a953bbf |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | Training corpora and preprocessing The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers. To obtain a high-quality training corpus, a cleaning pipeline with the following operations has been applied: - data parsing in different formats - se... | b72d5a603604fe8aa6d68610ff33bad1 |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | Evaluation The model has been fine-tuned on three Named Entity Recognition (NER) tasks using three clinical NER datasets: - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/). - [CA... | 459fd08a3158415995be59c9e60e7e71 |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | .YTt5qH2xXbQ). - ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. We addressed the NER task as a token classification problem using a standard linear layer along ... | da076fca02fb477c79d388f48de18fed |
apache-2.0 | ['biomedical', 'clinical', 'spanish'] | false | Citation information If you use these models, please cite our work: ```bibtext @inproceedings{carrino-etal-2022-pretrained, title = "Pretrained Biomedical Language Models for Clinical {NLP} in {S}panish", author = "Carrino, Casimiro Pio and Llop, Joan and P{\`a}mies, Marc and Guti{\'e}rre... | 9cab71a7e8e7e9a78845a50f44a25c7d |
apache-2.0 | ['generated_from_trainer'] | false | bert-engonly-sentiment-test 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: 0.4479 - Accuracy: 0.8967 | 3ff4ff63d2509ee783f696252d8b11fc |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | xls-npsc This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 48K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 3.5006 - Wer: 1.0 | 26ea9a37b2b253e0dd8d5ac9d5e26ff3 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | cd13f4cc0d5fc5d1582d5a9076beef8d |
apache-2.0 | ['generated_from_trainer'] | false | KB13-t5-base-finetuned-en-to-regex This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4785 - Semantic accuracy: 0.3902 - Syntactic accuracy: 0.3171 - Gen Len: 15.2927 | 0866b361efe166fff5803947c3038fae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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 - training_steps: 1000 | 0cca447df1b10b27f781c5300ace6cb3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Semantic accuracy | Syntactic accuracy | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:------------------:|:-------:| | No log | 2.13 | 100 | 0.7159 | 0.122 | 0.0976 | 15.24... | 1deec60b091b3fbe35c32063fbcb86bc |
mit | ['generated_from_trainer'] | false | gpt2-largeforbiddentoystory This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1643 | 8eb60ba82d6c503c084258b16548d4b9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 9 | edb6f0c81bbcc63ba883b7470b68c25b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 110 | 2.6384 | | No log | 2.0 | 220 | 2.1784 | | No log | 3.0 | 330 | 1.8316 | | No log | 4.0 | 440 | 1.5842 ... | a41634aa347f0298a8ffd991f12bc30d |
mit | ['generated_from_trainer', 'nlu', 'intent-classification'] | false | mdeberta-v3-base_amazon-massive_intent This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluation set: - Loss: 1.1661 - Acc... | ca614fdb969affd5a2934ff33868f503 |
mit | ['generated_from_trainer', 'nlu', 'intent-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 3.6412 | 1.0 | 720 | 2.7536 | 0.3123 | 0.3123 | | 2.8575 | 2.0 | 1440 | 1.8556 | 0.5303 | 0.5303 | | 1.7284 ... | 079f6df4fe300d3167d0b07c9c8b6429 |
mit | [] | false | model by Rodrigoajj This your the Stable Diffusion model fine-tuned the Rajj concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks man face** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.res... | 340fd52dedbce2bf7eb7bb6a67cb2ae6 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 | db09db95cff6d6f5a44ec9059c016d7e |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-rim_one-new This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4550 - Accuracy: 0.8767 | ff5642ceeb098e0dbd09d459d13e0b2a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.73 | 2 | 0.2411 | 0.9178 | | No log | 1.73 | 4 | 0.2182 | 0.8973 | | No log | 2.73 | 6 | 0.3085 | 0.... | 99ef889bab55f5ae0cde8711374f075d |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.6690 - Rouge1: 23.9405 - Rouge2: 5.0879 - Rougel: 18.4981 - Rougelsum: 18.5032 - Gen Len: 18.7376 | 4eedb62a8b415b080d1cd1bfbed55a50 |
apache-2.0 | ['generated_from_trainer', 'summarization'] | 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 - training_steps: 1000 - mixed_precision_training: Native AMP | 02b749dc608a57c7408029da6a0490ba |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.9249 | 0.08 | 1000 | 2.6690 | 23.9405 | 5.0879 | 18.4981 | 18.5032 | 18.73... | b762cd61caab803ac300c72dcb5118c1 |
apache-2.0 | ['translation'] | false | opus-mt-es-lus * source languages: es * target languages: lus * OPUS readme: [es-lus](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-lus/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 2287527d9841786706bbd4a6e738c785 |
apache-2.0 | ['mt5-small', 'text2text-generation', 'natural language generation', 'conversational system', 'task-oriented dialog'] | false | mt5-small-nlg-all-crosswoz This model is a fine-tuned version of [mt5-small](https://huggingface.co/mt5-small) on [CrossWOZ](https://huggingface.co/datasets/ConvLab/crosswoz) both user and system utterances. Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. | f53ae5467463fd22342ae711bbda1c64 |
apache-2.0 | ['mt5-small', 'text2text-generation', 'natural language generation', 'conversational system', 'task-oriented dialog'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 | 4f0b658c221fa6c5d28f963411fb18cd |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et a... | a62881c34dc25a3507b24d6fe676749a |
mit | [] | false | ggplot2 on Stable Diffusion This is the `<ggplot2>` 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... | db5a1edba6b5a4ffceed6c63c411edd2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-ver1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8669 | 6de9782118b46681bdddafc8cba768d2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6175 | 1.0 | 554 | 1.8621 | | 1.1951 | 2.0 | 1108 | 1.8669 | | 9c3414bea3928423b817204c73b57126 |
mit | ['glossbert'] | false | GlossBERT A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: '[GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/pdf/1908.07245.pdf)' Disclaimer: This model was bu... | b02e215c56e6575a0221ae42f80d4da0 |
mit | ['glossbert'] | false | Usage The following code loads GlossBERT: ```py from transformers import AutoTokenizer, BertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained('kanishka/GlossBERT') model = BertForSequenceClassification.from_pretrained('kanishka/GlossBERT') ``` | 0c30b6607a6f3e66c89b16cd12561c32 |
mit | ['glossbert'] | false | Citation If you use this model in any of your projects, please cite the original authors using the following bibtex: ``` @inproceedings{huang-etal-2019-glossbert, title = "{G}loss{BERT}: {BERT} for Word Sense Disambiguation with Gloss Knowledge", author = "Huang, Luyao and Sun, Chi and Qiu, Xip... | ddd3238ff4cbfaf404ba57f834e9ef68 |
mit | [] | false | gpt2-wechsel-swahili Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ | 483faa93210b4da224d4b1bb42fcf9ce |
mit | [] | false | RoBERTa | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-french` | **82.43** | **90.88** | **86.65** | | `camembert-base` | 80.88 | 90.26 | 85.57 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-german` | **81.79** | **89.72** | **85.... | 26bedf73ed94b86e3ed96b624a0d7707 |
mit | [] | false | GPT2 | Model | PPL | |---|---| | `gpt2-wechsel-french` | **19.71** | | `gpt2` (retrained from scratch) | 20.47 | | Model | PPL | |---|---| | `gpt2-wechsel-german` | **26.8** | | `gpt2` (retrained from scratch) | 27.63 | | Model | PPL | |---|---| | `gpt2-wechsel-chinese` | **51.97** | | `gpt2` (retrained from scra... | bd201910f4de0b49c74b65e63d3c5ca6 |
creativeml-openrail-m | [] | false | AniPlus v1 is a Stable Diffusion model based on a mix of Stable Diffusion 1.5, Waifu Diffusion 1.3, TrinArt Characters v1, and several bespoke Dreambooth models. It has been shown to perform favorably when prompted to produce a variety of anime and semi-realistic art styles, as well as a variety of different poses. T... | 7ebaba61980de0e2d809ab0b98561221 |
creativeml-openrail-m | [] | false | Samples *All samples were produced using the AUTOMATIC1111 Stable Diffusion Web UI @ commit ac085628540d0ec6a988fad93f5b8f2154209571.* ``` 1girl, school uniform, smiling, looking at viewer, portrait Negative prompt: nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, croppe... | 2948b24fb16fe6a718d6240f819f036a |
mit | ['ner'] | false | Description A Named Entity Recognition model trained on a customer feedback data using DistilBert. Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples: - PROD: for certain products - BRND: for brands - PERS: people names The following tags are simply in place... | 2fbc668e7150610637ce4686de426015 |
mit | ['ner'] | false | Usage ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil") model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil") ``` | 224fee51194baf67a9bb5dba93a5a4c9 |
afl-3.0 | [] | false | VLP Dataset Metadata This dataset was acquired during the dissertation entitled **Optical Camera Communications and Machine Learning for Indoor Visible Light Positioning**. This work was carried out in the academic year 2020/2021 at the Instituto de Telecomunicacoes in Aveiro. The images that constitute this dataset... | dac88f17421701038d46910f21ee7709 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2-target-glue-mnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6528 - Accuracy: 0.7271 | d6cbcbbef71ded17e0d8cf03cff38494 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9063 | 0.04 | 500 | 0.8249 | 0.6370 | | 0.8116 | 0.08 | 1000 | 0.7813 | 0.6619 | | 0.7724 | 0.12 | 1500 | 0.7504 | 0.... | d9c43026d7ffd0a568ceb195fcd44ff5 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-l-xlsr-es-col-pro-noise This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0677 - Wer: 0.0380 | 1b58de0bbc34ab89775595e6278f036d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.94 | 1.21 | 400 | 0.0800 | 0.0814 | | 0.4711 | 2.42 | 800 | 0.0730 | 0.0692 | | 0.3451 | 3.62 | 1200 | 0.0729 | 0.0669 | |... | 9cb186d134d891f7fa1bc4dbd5e124f9 |
apache-2.0 | ['generated_from_trainer'] | false | platzi-vit-model-tommasory-beans 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0343 - Accuracy: 0.9925 | d1d3b79548ae8d620f84e91bb6f6488c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1441 | 3.85 | 500 | 0.0343 | 0.9925 | | 032e028d184560643890ee5b5078a589 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_wavlm_s847 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled a... | e4de3e0cbb1dc5a19200c41245f62a07 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-najianews 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: 0.3788 - Accuracy: 0.9075 - F1: 0.9074 | ef3abeb5a7729ce2bf6a3284de043f36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4709 | 1.0 | 249 | 0.3247 | 0.8933 | 0.8898 | | 0.2174 | 2.0 | 498 | 0.3848 | 0.9004 | 0.8952 | | 0.1444 |... | 2398185048b521bed8ec024d35011fdd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-emotion-recognition'] | false | Prediction ```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor import librosa import IPython.display as ipd import numpy as np import pandas as pd ``` ```python device = torch.device("cuda" if torch.cuda.is_avai... | 1abbcb77fa91fed256e038b449949ab6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-emotion-recognition'] | false | Evaluation The following tables summarize the scores obtained by model overall and per each class. | Emotions | precision | recall | f1-score | accuracy | |:---------:|:---------:|:------:|:--------:|:--------:| | Anger | 0.95 | 0.95 | 0.95 | | | Fear | 0.33 | 0.17 | 0.22 | ... | b0c0317d259339d6598feb46f4b03cd4 |
wtfpl | [] | false | Marble statues with a hint of abstract. Works well with the words 'flower petals' and other embeds like PhotoHelper and Hyperfluid. The embedding is very strongly biased towards humans; requires some tinkering to get other results. Might make a v2 at some point that's more universal. Use FloralMarble-150.pt or FloralM... | f0b6e8e8633cd43be7530f98b3443ef1 |
mit | ['roberta-base', 'roberta-base-epoch_8'] | false | RoBERTa, Intermediate Checkpoint - Epoch 8 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 i... | f6ebceecb01bd6dc498d48cf4ea88e77 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_wav2vec2_s317 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 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | 8a004b446929581932a30bcd3d7df185 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Usage To use this LoRA you have to download the file, as well as drop it into the "\stable-diffusion-webui\models\Lora" folder To use it in a prompt, please refer to the extra networks panel in your Automatic1111 webui. I highly recommend using it at around 0.4 to 0.6 strength for the best results. If you'd like to ... | 389360b74d65eb60c155d1bbe414861a |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'lora'] | false | Example Pictures <table> <tr> <td><img src=https://i.imgur.com/2aiatls.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/HWMhTUt.png width=50% height=100%/></td> </tr> <tr> <td><img src=https://i.imgur.com/hBelYEF.png width=50% height=100%/></td> </tr> </table> | 25cb81556331334bfeaac993ef3b8296 |
cc-by-4.0 | [] | false | `cyT5-small` is a light-weight (alpha-version) Welsh T5 model extracted from the `google/mt5-small` model and fine-tuned only on the [Welsh summarization dataset](https://huggingface.co/datasets/ignatius/welsh_summarization). **Citation:** [Introducing the Welsh Text Summarisation Dataset and Baseline Systems](https... | e08f2362909252b6d0221113f6f468ed |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-zh-hk This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 zh-HK dataset. It achieves the following results on the evaluation set: - Loss: 0.3003 - Wer: 0.5615 | 2f04e830fc99b5aa927d1fbf068b5413 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=... | 1e2acb7ca5e1d2ae7602cbc8097427f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1556 | 2.28 | 1000 | 0.2708 | 0.6069 | | 0.038 | 4.57 | 2000 | 0.2674 | 0.5701 | | 0.0059 | 6.85 | 3000 | 0.2843 | 0.5635 | |... | 8de424f81315e58de9e7b63b31100a6b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2 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: 0.7027 - Accuracy: 0.5092 | 40cf28010e85f187dff3a77ee587d53d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - 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: 5 | 92473e60036f166990db1565b013c17e |
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