license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8175 | 1.0 | 70 | 0.3331 | 0.7147 | | 0.2807 | 2.0 | 140 | 0.2745 | 0.8045 | | 0.1836 | 3.0 | 210 | 0.2562 | 0.8223 | ... | 4c93000afc457dc9f04760892910da97 |
mit | ['Stable Diffusion', 'Senko', 'Hypernetwork'] | false | Description This hypernetwork will help you to make your Senko-san be look like she was drawn by Rimukoro. This model was trained using [any222trinart](https://huggingface.co/MindB1ast/any222trinart/blob/main/any222trinart.ckpt) model (also known as Cabbage Mix) so it should work fine with that specific model or with... | 53cfa912c979d711e2d4cdd8b5b33ed4 |
mit | ['Stable Diffusion', 'Senko', 'Hypernetwork'] | false | Usage For using this hypernetwork just place .pt file in your `models\hypernetworks` directory and then depends on your UI you will need to choose this hypernetwork in settings or use it directly in your positive prompt like `<hypernet:cab_senko_by_rimukoro_4000:1.0>`. Make sure that `cab_senko_by_rimukoro_4000` fits... | 884a68e3e16a1c44693b0c8af1d12bd3 |
['cc0-1.0'] | ['gan', 'generative adversarial networks', 'deep dream'] | false | Keras Implementation of Deep Dream 🦚🌌 This repo contains the model and the notebook [for this Deep Dream implementation of Keras](https://keras.io/examples/generative/deep_dream/). Full credits to: [François Chollet](https://twitter.com/fchollet) ![deepdream](https://keras.io/img/examples/generative/deep_dream/d... | 6e1103fd8b9f2969393a719ad97ad377 |
['cc0-1.0'] | ['gan', 'generative adversarial networks', 'deep dream'] | false | Background Information "Deep dream" is an image-filtering technique which consists of taking an image classification model, and running gradient ascent over an input image to try to maximize the activations of specific layers (and sometimes, specific units in specific layers) for this input. It produces hallucinatio... | fca516bd25580dde660d250dd79f3134 |
mit | [] | false | ricar on Stable Diffusion This is the `<ricard>` 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 yo... | 7412ff1906cd7c51fefc2e1c05b1ad68 |
apache-2.0 | ['generated_from_trainer'] | false | presentation_emotion_31415 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: 1.1243 - F1: 0.7149 | a40f1248c1d134855619e2e52b847005 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.18796906442746e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 43605e93942866c01df9de31297df877 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.73 | 1.0 | 408 | 0.8206 | 0.6491 | | 0.3868 | 2.0 | 816 | 0.7733 | 0.7230 | | 0.0639 | 3.0 | 1224 | 0.9962 | 0.7101 | |... | 0b1dbd4e823f5289aeb65f39b5c35905 |
apache-2.0 | ['translation'] | false | opus-mt-lus-fr * source languages: lus * target languages: fr * OPUS readme: [lus-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lus-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | f139447336569218a1ae4348b6a355cb |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dat... | 96a679ea6935df255e2de3847beb5f7c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gl', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-galician This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GL dataset. It achieves the following results on the evaluation set: - Loss: 0.1525 - Wer: 0.1542 | 00efe90ef8a040da2eb1fa4cb8b2961d |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gl', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 20.0 - mixed_precision_... | b35a35fdbd818641264f75c3fd50168b |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'gl', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0067 | 4.35 | 500 | 2.9632 | 1.0 | | 1.4939 | 8.7 | 1000 | 0.5005 | 0.4157 | | 0.9982 | 13.04 | 1500 | 0.1967 | 0.1857 | |... | cd1d7e4510366a97ff89f9410a206e15 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the duolingo concept trained by avojarot on the avojarot/duolingo_owl dataset. This is a Stable Diffusion model fine-tuned on the duolingo concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of duolingo owl** This model was created as part of the DreamBooth Hack... | 5752f37a584ecf4732b3551ef3d9eedb |
mit | ['generated_from_trainer'] | false | model_from_berturk_upos_22Jan This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0130 - Precision: 0.9961 - Recall: 0.9953 - F1: 0.9957 - Accuracy: 0.9967 | d30049d3665b7ac6aa6b9f56501c900e |
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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | cc233a7383c4e663ae06bcdfb7b955e7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 170 | 0.2285 | 0.9100 | 0.9079 | 0.9089 | 0.9352 | | No log | 2.0 |... | cf1801a894b9c20d62498c0ca2aa562f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | animecharacters Dreambooth model trained by anmol-chawla with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast... | 0e67aa6eed196fcb05a42478dd9a1bb3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2867 - F1: 0.8355 | 1f1a540e9b6bfe3eda7335fe13eebec7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5817 | 1.0 | 191 | 0.3395 | 0.7854 | | 0.2617 | 2.0 | 382 | 0.2856 | 0.8278 | | 0.1708 | 3.0 | 573 | 0.2867 | 0.8355 | ... | 7c7175f2d1c0f5742e41e677c8b1578b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-16-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6012 - Accuracy: 0.6766 | 5aebed8b4bdf42d91637b27ac7bc6175 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6983 | 1.0 | 7 | 0.7036 | 0.2857 | | 0.6836 | 2.0 | 14 | 0.7181 | 0.2857 | | 0.645 | 3.0 | 21 | 0.7381 | 0.... | e9e5e8d00724536abf0a1343552e8b7d |
apache-2.0 | ['bag-of-words', 'dense-passage-retrieval', 'knowledge-distillation'] | false | Uni-ColBERTer (Dim: 1) for Passage Retrieval
If you want to know more about our (Uni-)ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉
For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter
| c09eaf4ec594db99462f989d65032f34 |
apache-2.0 | ['bag-of-words', 'dense-passage-retrieval', 'knowledge-distillation'] | false | Limitations & Bias
- The model is only trained on english text.
- The model inherits social biases from both DistilBERT and MSMARCO.
- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
| 480297c6c2fa657ae4b116ecaa81f059 |
apache-2.0 | ['bag-of-words', 'dense-passage-retrieval', 'knowledge-distillation'] | false | Citation
If you use our model checkpoint please cite our work as:
```
@article{Hofstaetter2022_colberter,
author = {Sebastian Hofst{\"a}tter and Omar Khattab and Sophia Althammer and Mete Sertkan and Allan Hanbury},
title = {Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interacti... | f631abe7c15922813bd0a2dfd65bad6b |
apache-2.0 | ['generated_from_keras_callback'] | false | himanshusrtekbox/distilbert-base-uncased-finetuned-cola 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: 0.1911 - Validation Loss: 0.5605 - Train Matthews Correl... | dddcfab72a502ce28e9de711eff053f6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5185 | 0.4556 | 0.4728 | 0 | | 0.3247 | 0.4570 | 0.5093 | 1 | | 0.1911 | 0.5605... | 88f0c9f9c3b1a102866756d8e5233dd0 |
apache-2.0 | ['vision', 'image-classification'] | false | LeViT LeViT-128 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). Disc... | 23160c71054ee6ee95cd7ec053c6b64f |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import LevitFeatureExtractor, LevitForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.j... | dca9efa643266074c8c20303ab70fe96 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_wermet_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5820 - Train Accuracy: 0.0305 - Train Wermet: 1.5323 - Validation Loss: 0.6980 - Validation Accura... | 2b10168bebf8bbf011f1138399c7ec5e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0795 | 0.0116 | 43.8776 | 4.4395 | 0.0122... | 3b84ade415c2b3908262c6af190c2da0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.03 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | e473ca55907ef6dd29c3f27deca14b7c |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-0_sixties-10_s131 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | ad1ec35ea539282583f671f421c35195 |
cc-by-4.0 | ['generated_from_trainer'] | false | nb-bert-base-ctr-regression This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0073 - Mse: 0.0073 | b6a6ad065a8d6bb88641820cd677095e |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_tr... | 493f2bf38976b54f583abde350db34b9 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0106 | 1.0 | 1103 | 0.0069 | 0.0069 | | 0.0073 | 2.0 | 2206 | 0.0072 | 0.0072 | | 0.0058 | 3.0 | 3309 | 0.0063 | 0.006... | b0f979f4534fb2109835c93ffd17fb03 |
mit | ['text-classification'] | false | Multi2ConvAI-Logistics: German logistic regression model using fasttext embeddings
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- l... | 2c3950e7558aff3582981e7d9f0e6e30 |
mit | ['text-classification'] | false | How to run
Requires:
- [multi2convai](https://github.com/inovex/multi2convai)
- serialized fastText embeddings (see last section of this readme or [these instructions](https://github.com/inovex/multi2convai/models/embeddings.README.md))
| fe0c8d1565d3e2a6ada3bf52364d4763 |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
python scripts/run_inference.py -m multi2convai-logistics-de-logreg-ft
>>> Create pipeline for config: multi2convai-logistics-de-logreg-ft.
>>> Created a LogisticRegressionFasttextPipeline for domain: 'logistics' and language 'de'.
>>>
>>> Enter... | 4d61025e478a55b44321f69ad906eb88 |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
from pathlib import Path
from multi2convai.pipelines.inference.base import ClassificationConfig
from multi2convai.pipelines.inference.logistic_regression_fasttext import (
LogisticRegressionFasttextConfig,
LogisticRegressionFasttextPipeli... | 8e3546f9679747180c66312c92248972 |
mit | ['text-classification'] | false | 1. Define paths of model, label dict and embeddings
model_file = "model.pth"
label_dict_file = "label_dict.json"
embedding_path = Path(
f"../models/embeddings/fasttext/de/wiki.200k.de.embed"
)
vocabulary_path = Path(
f"../models/embeddings/fasttext/de/wiki.200k.de.vocab"
)
| ec81fb2ffe042e973210e455e705e13d |
mit | ['text-classification'] | false | 2. Create and setup pipeline
model_config = LogisticRegressionFasttextConfig(
model_file, embedding_path, vocabulary_path
)
config = ClassificationConfig(language, domain, label_dict_file, model_config)
pipeline = LogisticRegressionFasttextPipeline(config)
pipeline.setup()
| 516a1c3040e3d1923dd63e028420d183 |
mit | ['text-classification'] | false | assumes working dir is the root of the cloned multi2convai repo
mkdir models/fasttext/de
curl https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.de.vec --output models/fasttext/de/wiki.de.vec
python scripts/serialize_fasttext.py -r fasttext/wiki.de.vec -v fasttext/de/wiki.200k.de.vocab -e fasttext/de/wik... | 41ecd298a7e405fc9935b7297fed7678 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-it_s878 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 3f61dabf46dbbba35420ac9563bb2168 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-0_belgium-10_s947 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When usi... | 3062a3a16a6ce2a91822dcc7f30fcfd9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon... | e935a3c48fb330913fb26fa272b0f953 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_ner_wnut_17 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2400 - Precision: 0.6701 - Recall: 0.5467 - F1: 0.6021 - Accuracy: 0.9559 | 1c7c5f6d25df2339edcce47570456e88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2367 | 0.6879 | 0.4270 | 0.5269 | 0.9455 | | No log | 2.0 |... | fbf627069bd0878a0ba13cc986bcb623 |
mit | [] | false | flatic on Stable Diffusion This is the `<flat-ct>` 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 ... | 40a8bf2896f5630d333cf792577f7cc0 |
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: 0.0610 - Precision: 0.9275 - Recall: 0.9370 - F1: 0.9322 - Accuracy: 0.9836 | c7f8287d7b359ea02ecbf31b8efc6bdc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2507 | 1.0 | 878 | 0.0714 | 0.9181 | 0.9243 | 0.9212 | 0.9813 | | 0.0516 | 2.0 |... | df2d08dbe23e97e2705949bb2a0abf53 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-nl_s885 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | f807e0845290bb3599fed68fcb23f9b6 |
mit | ['text-generation', 'novel-generation', 'fiction', 'gpt-neo', 'pytorch'] | false | Usage ``` from transformers import pipeline model_name: str = 'FrostAura/gpt-neo-1.3B-fiction-novel-generation' generator: pipeline = pipeline('text-generation', model=model_name) prompt: str = 'So far my day has been ' gen_text: str = generator(prompt, do_sample=True, min_length=50) print(f'Result: {gen_text}') ``... | 2c369f778ae48fd63dbd3a6957db02f4 |
apache-2.0 | ['translation'] | false | opus-mt-en-lu * source languages: en * target languages: lu * OPUS readme: [en-lu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | db494900e7c6a26920797251d98fa937 |
apache-2.0 | ['summarization'] | false | Longformer Encoder-Decoder (LED) fine-tuned on ILC This model is a fine-tuned version of [led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [ILC](https://huggingface.co/datasets/d0r1h/ILC) dataset. As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) ... | 97d7a3a7cad5eca511fc11ce5b68b9b7 |
apache-2.0 | ['summarization'] | false | Evaluation results When the model is used for summarizing ILC documents(10 samples), it achieves the following results: | Model | rouge1-f | rouge1-p | rouge2-f | rouge2-p | rougeL-f | rougeL-p | |:-----------:|:-----:|:-----:|:------:|:-----:|:------:|:-----:| | led-ilc | **42** | **47*... | 9c169c48f2d06bd97635edf8f7b7fb57 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-mnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4218 - Accuracy: 0.8488 | 993d9947be19f7eb8bedf4c80093b409 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5194 | 1.0 | 3068 | 0.4468 | 0.8307 | | 0.3445 | 2.0 | 6136 | 0.4384 | 0.8428 | | 0.2341 | 3.0 | 9204 | 0.4946 ... | 6075d6d80bfc646f27316344356ebb2f |
apache-2.0 | ['vision'] | false | Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et a... | 6f289da9845e7f33bdebaf908cfeec8b |
apache-2.0 | ['vision'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are li... | 74b869a8bcbff09dc1c75ecc2f34d081 |
apache-2.0 | ['vision'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import ViTFeatureExtractor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.f... | 86a3109cc459a6dbab9324d1af3123b2 |
mit | ['deberta', 'deberta-v3', 'fill-mask'] | false | DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of ... | 0d460bedb14c510bd1e51c8473813251 |
mit | ['deberta', 'deberta-v3', 'fill-mask'] | false | Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)| |-------------------|----------|-------------------|-----------|----------| | RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- | | XLNet-base |32 |92 | -/80.2 | 86.8/- | | ELECTRA-base |30 |86 | -/... | 342abccbb20b77c79c43be69e549e6ef |
mit | ['deberta', 'deberta-v3', 'fill-mask'] | false | !/bin/bash cd transformers/examples/pytorch/text-classification/ pip install datasets export TASK_NAME=mnli output_dir="ds_results" num_gpus=8 batch_size=8 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \ run_glue.py \ --model_name_or_path microsoft/deberta-v3-xsmall \ --task_name $TASK_NAM... | c3d9153091bc566a68957a55c6d60b83 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 72 - eval_batch_size: 72 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 0867720f5c865bb958dc19b7d5301d89 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_10_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6912 - F1: 0.7977 | 4a4150e95508c3fa174b13ffdf801005 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4002 | 0.8012 | | 0.4056 | 2.0 | 576 | 0.4372 | 0.8075 | | 0.4056 | 3.0 | 864 | 0.4720 | 0.8071 | |... | 32b88b888a404ae8494b8a3641541893 |
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: - eval_loss: 0.0593 - eval_precision: 0.9293 - eval_recall: 0.9485 - eval_f1: 0.9388 - eval_accuracy: 0.9858 - eval_runt... | be37317dd0ec7ccbd57ca33966d9b3a2 |
apache-2.0 | [] | false | The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | 5234d95b4fa2e704a74134c21b63baf9 |
apache-2.0 | [] | false | 8209;NCC|[🤗](https://huggingface.co/north/t5_small_NCC)|[🤗](https://huggingface.co/north/t5_base_NCC)|[🤗](https://huggingface.co/north/t5_large_NCC)|[🤗](https://huggingface.co/north/t5_xl_NCC)|[🤗](https://huggingface.co/north/t5_xxl_NCC)|| |North-T5& | a6e1097c9405fa97d659e93a5d200c56 |
apache-2.0 | [] | false | 8209;lm|[🤗](https://huggingface.co/north/t5_small_NCC_lm)|[🤗](https://huggingface.co/north/t5_base_NCC_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)|✔|| | 863d3bcc23ce342a3a0fbfee8e294b18 |
apache-2.0 | [] | false | Performance A thorough evaluation of the North-T5 models is planned, and I strongly recommend external researchers to make their own evaluation. The main advantage with the T5-models are their flexibility. Traditionally, encoder-only models (like BERT) excels in classification tasks, while seq-2-seq models are easier ... | 4743a3e8a149473ed71750f928785490 |
apache-2.0 | [] | false | 8209;NCC** |This is the main version. It is trained an additonal 500.000 steps on from the mT5 checkpoint. The training corpus is based on [the Norwegian Colossal Corpus (NCC)](https://huggingface.co/datasets/NbAiLab/NCC). In addition there are added data from MC4 and English Wikipedia.| |**North& | 7151381e3b92edcf0999dae387c08fa5 |
apache-2.0 | [] | false | 8209;lm**|The model is pretrained for an addtional 100k steps on the LM objective discussed in the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf). In a way this turns a masked language model into an autoregressive model. It also prepares the model for some tasks. When for instance doing translation and NLI, it is we... | c7ef5f9d2b868e85e9e7bc701023755e |
apache-2.0 | [] | false | Fine-tuned versions As explained below, the model really needs to be fine-tuned for specific tasks. This procedure is relatively simple, and the models are not very sensitive to the hyper-parameters used. Usually a decent result can be obtained by using a fixed learning rate of 1e-3. Smaller versions of the model typi... | 2607a6ca535f249041e117bbd04386b9 |
apache-2.0 | [] | false | Training details All models are built using the Flax-based T5X codebase, and all models are initiated with the mT5 pretrained weights. The models are trained using the T5.1.1 training regime, where they are only trained on an unsupervised masking-task. This also means that the models (contrary to the original T5) need... | f75f69737eeacd858be24f8883a86395 |
apache-2.0 | [] | false | Formats All models are trained using the Flax-based T5X library. The original checkpoints are available in T5X format and can be used for both finetuning or interference. All models, except the XXL-model, are also converted to Transformers/HuggingFace. In this framework, the models can be loaded for finetuning or infe... | 52cc93206c7d73b3551c1fb760e72b15 |
apache-2.0 | [] | false | Thanks This release would not have been possible without getting support and hardware from the [TPU Research Cloud](https://sites.research.google/trc/about/) at Google Research. Both the TPU Research Cloud Team and the T5X Team has provided extremely useful support for getting this running. Freddy Wetjen at the Nati... | 6968c910b846f1c30122dd524cce73c6 |
cc-by-sa-4.0 | [] | false | How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese')
>>> s... | 80ba92119ae72b3e26f0f89e90cbd45b |
cc-by-sa-4.0 | [] | false | Preprocessing
The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining.
The model was trained on 8 NVIDIA A100 GPUs. | badcb042a8c0a04a2e01c771d6c03c8c |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [PLOD-filtered](surrey-nlp/PLOD-filtered) dataset. It achieves the following results on the evaluation set: - Loss: 0.1148 - Precision: 0.9645 - Recall: 0.9583 - F1: 0.9614 - Accuracy: 0.9576 | acfb29f1a6a5a3587ee064335cecddce |
mit | ['generated_from_trainer'] | false | Training and evaluation data The model is fine-tuned using [PLOD-Filtered](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) dataset. This dataset is used for training and evaluating the model. The PLOD Dataset is published at LREC 2022. The dataset can help build sequence labeling models for the task of Abbr... | efd514656c39cc9a6837419d86bfb44a |
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: 6 | 1ce6b820ef18c67839ce6804250dc74a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1179 | 1.99 | 7000 | 0.1130 | 0.9602 | 0.9517 | 0.9559 | 0.9522 | | 0.0878 | 3.98... | c1f97b5820161f235854240f2fe2c440 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-NL8 (Deep-Narrow version) T5-Efficient-LARGE-NL8 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... | 1420cdf9d804bdaa1db62004558e72e8 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-nl8** - is of model type **Large** with the following variations: - **nl** is **8** It has **267.84** million parameters and thus requires *ca.* **1071.37 MB** of memory in full precision (*fp32*) or **535.69 MB** of memory in half precision (... | 83860c149351b1bfc759286d81625638 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 5aaa4c0bb4111245ecfb5ea1f61688a2 |
mit | ['generated_from_trainer'] | false | tst-summarization This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9975 - Rouge1: 56.239 - Rouge2: 28.9873 - Rougel: 38.5242 - Rougelsum: 53.7902 -... | 416b9d0f183bcc6d016d7ef4dc0d07d4 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | d2813de86dbd74a618fc2beece016f10 |
mit | [] | false | 001glitch_core on Stable Diffusion This is the `001glitch_core` 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... | 81b96a4f84fe2e7f6beeb370ecfe4a39 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-100k_s169 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 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 54b002615b5cc3382176a6ee0f2aac41 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-comp2 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: 0.9570 - Precision: 0.5169 - Recall: 0.6765 - F1: 0.5820 - Accuracy: 0.5820 | 963de1ab9862487c49c1f31d07c147a6 |
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: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | aa0bfbc80c7ed0253655f02815aa6990 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.8434 | 1.0 | 934 | 0.7147 | 0.4475 | 0.6252 | 0.5096 | 0.5096 | | 0.6307 | 2.0 |... | 0640ee5e3a252c2898cdb9e1880c29da |
mit | ['spacy', 'token-classification'] | false | ru_core_news_lg Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `ru_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologiz... | 04eca51f18b450e8b39bda053e3db0fe |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (900 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past... | 60983e4043a60bfcb701b08fef4e2fe3 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.68 | | `TOKEN_P` | 97.28 | | `TOKEN_R` | 98.31 | | `TOKEN_F` | 97.79 | | `POS_ACC` | 98.93 | | `MORPH_ACC` | 97.49 | | `MORPH_MICRO_P` | 98.97 | | `MORPH_MICRO_R` | 98.30 | | `MORPH_MICRO_F` | 98.64 | | `SENTS_P` | 99.87 | | `SENTS_R` | 99.85 | | `SENTS_F` | ... | d546bc1aa82bb233f924c4746fd61a75 |
apache-2.0 | ['vision', 'image-classification'] | false | LeViT LeViT-384 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). Disc... | 2e035a520169823448490f213dd1a915 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import LevitFeatureExtractor, LevitForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.j... | 58559148c46bbc458e35bad2ab31afe6 |
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