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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2377 | 1.0 | 878 | 0.0711 | 0.9176 | 0.9254 | 0.9215 | 0.9813 | | 0.0514 | 2.0 |... | b5dccfcbb9288c88d500e4ae405f5a57 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-squad-seed-420 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.8281 | 22784fb16f324a998f675403f6cb3465 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8445 | 1.0 | 8248 | 0.8337 | | 0.5828 | 2.0 | 16496 | 0.8281 | | 581385b501aaa6c785328ca79e0ea107 |
apache-2.0 | ['audio', 'audio-classification', 'speech'] | false | prediction ~~~ 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 ~~~ ~~~ device = torch.device("cuda" if torch.cuda.is_available() else "c... | 134abc0bcf96adaebf53a6b55e4c3be5 |
apache-2.0 | ['audio', 'audio-classification', 'speech'] | false | path for a sample path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' outputs = predict(path, sampling_rate) ~~~ ~~~ [{'Emotion': 'anger', 'Score': '78.3%'}, {'Emotion': 'disgust', 'Score': '11.7%'}, {'Emotion': 'fear', 'Score': '5.4%'}, {'Emotion': 'happiness', 'Score': '4.1%'}, {'Emotion': 'sadness', 'Score':... | bf25350a17c3cf6274a0f63b818c9124 |
apache-2.0 | ['audio', 'audio-classification', 'speech'] | false | Evaluation The following tables summarize the scores obtained by model overall and per each class. | Emotions | precision | recall | f1-score | accuracy | |-----------|-----------|--------|----------|----------| | anger | 0.82 | 1.00 | 0.81 | | | disgust | 0.85 | 0.96 | 0.85 | ... | ec21de12dcbc8e515771feaf2e2a98d0 |
mit | ['generated_from_trainer'] | false | xlnet-base-cased-finetuned-squad This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1093 | 070b38ac45b54273e3ab43fa0e4e3bed |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 203 | 0.2186 | | No log | 2.0 | 406 | 0.1985 | | 0.4204 | 3.0 | 609 | 0.1093 | | 9a8effb4810f70eed2c68da0b9e9c643 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-multilingual-uncased-oct-3 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: 0.0532 - F1: 0.9073 | 49e695a51490abce8e408b5fec6bba22 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1205 | 1.0 | 565 | 0.0662 | 0.8449 | | 0.0524 | 2.0 | 1130 | 0.0535 | 0.8921 | | 0.033 | 3.0 | 1695 | 0.0532 | 0.9073 | ... | 1688c4bf10248e7585c7dae7681f5da3 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | WhisperSmallSwedishBirgerMoell This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3253 - Wer: 19.5854 | 4fde79e7e4070bf3d842342668b9b0cc |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1523 | 1.29 | 1000 | 0.2924 | 21.5509 | | 0.0515 | 2.59 | 2000 | 0.2856 | 20.4593 | | 0.0214 | 3.88 | 3000 | 0.3010 | 19.905... | 47d3e5d5f9084e6bc79e8af919883edf |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'DA'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-en_any", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["Reduc... | 492b2bb1eaa064e2e18771fe32056984 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8559 - Bleu: 52.9456 | 46ca86f648a80a0acbce79bc94521062 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'myv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | 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_8_0 - MYV dataset. It achieves the following results on the evaluation set: - Loss: 1.0356 - Wer: 0.6524 | 0c205df1d4d1aac0c4070151264df879 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'myv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands **1. To evaluate on mozilla-foundation/common_voice_8_0 with test split** python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-myv-a1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs **2. To evaluate on speech-recognition-community-v2/dev_data** Erzya lan... | ba4470c045d16bb0b8ccd342726d6ba1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'myv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - 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: 800 - num_epochs: 200.0 - mixed_precisio... | 15ad967dd1761bc6ab83a2b517c618bc |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'myv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 5.649 | 9.62 | 500 | 3.0038 | 1.0 | | 1.6272 | 19.23 | 1000 | 0.7362 | 0.7819 | | 1.1354 | 28.85 | 1500 | 0.6410 | ... | 831bd2b72598aa95ff0fb440009191c4 |
apache-2.0 | [] | false | klue-cross-encoder-v1 - klue/bert-base 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델 - This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. | 287442c683ad2e8a44ea1c9e1cd5374b |
apache-2.0 | [] | false | Training - sts(10)-sts(10)훈련 시킴 - STS : seed=111,epoch=10, lr=1e-4, eps=1e-6, warm_step=10%, max_seq_len=128, train_batch=128(small 모델=32) (albert 13m/7G) [훈련코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-nli.ipynb) - [평가코드](https://github.com/kobongsoo/BERT/blob/mast... | d57d1494f12074dd5088b74bde51fe78 |
apache-2.0 | [] | false | Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('bongsoo/kpf-cross-encoder-v1') scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')]) print(scores) ``` ``` [0.10161418 0.45563662] ``` The model will pr... | 483453117dd75505701e32d918905b45 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Greek - Robust This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 0.3168 - Wer: 21.6846 | eee6dfb037ecb84046e21e46fecaaa93 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precisio... | b62d1cc3d2f1e73029978a2b124674b1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3865 | 1.17 | 500 | 0.5842 | 51.4487 | | 0.2302 | 2.35 | 1000 | 0.4861 | 39.3202 | | 0.1321 | 3.52 | 1500 | 0.4536 | 37.425... | 2122caad9d9f59b8467e8f800d389e25 |
mit | ['Long documents', 'longformer', 'robertalex', 'spanish', 'legal'] | false | Legal ⚖️ longformer-base-4096-spanish `legal-longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**[RoBERTalex](https://huggingface.co/PlanTL-GOB-ES/RoBERTalex)** in this case) and pre-trained for *MLM* on long documents from the [Spanish Legal Domain Corpora](https://zenodo.org/record/549... | 21712522e8317e9c46b523403d876f70 |
mit | ['Long documents', 'longformer', 'robertalex', 'spanish', 'legal'] | false | .Y205lpHMKV5). It supports sequences of length up to **4,096**! **Longformer** uses a combination of a sliding window (*local*) attention and *global* attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations. This model was made following the research... | d1403a1bc6e1149d29e9180cafa510b8 |
mit | ['Long documents', 'longformer', 'robertalex', 'spanish', 'legal'] | false | Model (base checkpoint) [RoBERTalex](https://huggingface.co/PlanTL-GOB-ES/RoBERTalex?) There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient in solving se... | c31cf5701be0b0f61e772e3f647146d5 |
mit | ['Long documents', 'longformer', 'robertalex', 'spanish', 'legal'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{narrativa2022legal-longformer-base-4096-spanish, title={Legal Spanish LongFormer by Narrativa}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/Narrativa/legal-l... | fef183fdc39a167d4b17d51d813009bc |
mit | ['Long documents', 'longformer', 'robertalex', 'spanish', 'legal'] | false | Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems b... | 8d6248b54cbbcb44d9f837be4be01ab8 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 140k (uncased) Seed 1 intermediate checkpoint 140k 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/goo... | 853d8fdfa8c0c259c8db47b9e85a298e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | 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-1-140k') model = BertModel.from_pretrained("multiberts-seed-1-140k") text = "Replace me by any text you'd like.... | 4e5dc26d0b54e7ff3c5c559fb7d1fef7 |
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: 0.0619 - Precision: 0.9379 - Recall: 0.9527 - F1: 0.9452 - Accuracy: 0.9867 | 37a084c706e5bef26e801766918eb1c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.088 | 1.0 | 1756 | 0.0625 | 0.9203 | 0.9399 | 0.9300 | 0.9835 | | 0.0383 | 2.0 |... | dc1cd0831789b107ab948bdaeb997bf2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | f1935266911045138e5a746a06a50dc6 |
mit | ['text-to-image'] | false | Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a f8 VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps. The models in these repo refer to the "Arroz con Cosas" variation of Paella that provide: * A clip2img model, to turn CLIP image ... | 6cf7b78eace0882673eb230a8393e488 |
mit | [] | false | Titan Robot on Stable Diffusion This is the `<titan>` 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 tra... | 646221d895e411959ae15f2c4fe56d25 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-requirements This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5811 - Precision: 0.7722 - Recall: 0.6854 - F1: 0.7262 - Accuracy: 0.8297 | 5c45b45bc04dc83106f31bc44143a736 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.8961 | 0.7234 | 0.3820 | 0.5 | 0.6865 | | No log | 2.0 |... | c429b9688d398815c9ed78b1ad661553 |
apache-2.0 | ['translation'] | false | opus-mt-iso-sv * source languages: iso * target languages: sv * OPUS readme: [iso-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/iso-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | a265bded765478cf9cfdbe7ac5c22d00 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-mic-sym This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0023 - Accuracy: 0.9997 - F1: 0.9997 | a0fb324d44b91ae935c44aa6b4a646d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 188 | 0.0049 | 0.9990 | 0.9990 | | No log | 2.0 | 376 | 0.0023 | 0.9997 | 0.9997 | | 142fba479428a35c6470c187a737356f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-lt-en Neural machine translation model for translating from Lithuanian (lt) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | 35e53e5c233d806cebafc7fd7f1611bf |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-02-25 * source language(s): lit * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](htt... | ab87d44a3afeb3f340a01f7ad4e5e575 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Katė sedėjo ant kėdės.", "Jukiko mėgsta bulves." ] model_name = "pytorch-models/opus-mt-tc-big-lt-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(mod... | f14b4bf9275f0a6c0f7e601cba73c20c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Yukiko likes potatoes. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-lt-en") print(pipe("Katė sedėjo ant kėdės.")) | d752fae8bdd97544e3b770569dd26397 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lit-eng/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | c638ba300b33a7b8864f604e4e864421 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | lit-eng | tatoeba-test-v2021-08-07 | 0.74881 | 61.6 | 2528 | 17855 | | lit-eng | flores101-devtest | 0.60662 | 34.3 | 1012 | 24721 | | lit-eng | newsdev2019 | 0.59995 | 32.9 | 2000 | 49312 | | lit-eng | newstest2019 | 0.61742 | 32.3 | 1000 | 25878 | | 18b01a9a08d8e16dd1ee591fbdcd0319 |
mit | ['generated_from_trainer'] | false | bart_large-tldr-news This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1949 - Rouge1: 20.747 - Rouge2: 8.4086 - Rougel: 17.4662 - Rougelsum: 18.1462 - Gen Len: 70.6259 ... | 4b64e082ac7b2b0b4fc1e6566ae74cc3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 0.3034 | 1.0 | 893 | 2.1949 | 20.747 | 8.4086 | 17.4662 | 18.1462 | 70.6259 ... | 506ebfa59256b347907068e99303f285 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013 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 image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8504 - Accuracy: 0... | 6924919282f73750697a7b3df0781094 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1617 | 1.0 | 202 | 1.0081 | 0.6270 | | 1.0604 | 2.0 | 404 | 0.9516 | 0.6524 | | 0.998 | 3.0 | 606 | 0.8857 | 0.... | 076f6e0e18028428c99d27826954ea7a |
apache-2.0 | ['generated_from_keras_callback'] | false | HanSSH/mt5-small-finetuned-amazon-en-es-0920 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: 3.0998 - Validation Loss: 3.2948 - Epoch: 3 | f6592017309eb21affc5ee37918ad675 |
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': 0.00056, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | eb527a2e324d11b224ff72e3a7deda2c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.9659 | 3.6380 | 0 | | 3.8434 | 3.4036 | 1 | | 3.3614 | 3.3487 | 2 | | 3.0998 | 3.2948 | 3 | | 62bf1fd96dcbabf1265b99bcd79f009f |
cc-by-sa-4.0 | ['long-documents'] | false | Model description This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). The model has been warm-started re-using the weights of RoBERTa (L... | f723d21e36da74ab04370fae8ccf7cae |
cc-by-sa-4.0 | ['long-documents'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT or fine-tuned versions on a task that interests you. ... | abb4b6d6e983c14266301d2f3471c7e0 |
cc-by-sa-4.0 | ['long-documents'] | false | How to use You can use this model directly for masked language modeling: ```python from transformers import AutoTokenizer, AutoModelForForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True) mlm_model = AutoModelForMaskedLM("kiddothe2b/hierarchic... | c15dbe55899e733e3be326df660196d6 |
cc-by-sa-4.0 | ['long-documents'] | false | Training and evaluation data The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint and has been continued pre-trained for additional 50k steps in long sequences (> 1024 subwords) of [C4](https://huggingface.co/datasets/c4) (Raffel et al., 2020). | 8af02f5eb5f1509bc5dfeb0568898c65 |
cc-by-sa-4.0 | ['long-documents'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: tpu - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with bet... | 9bac672c07103fb9a329226d9941c628 |
cc-by-sa-4.0 | ['long-documents'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7437 | 0.2 | 10000 | 1.6370 | | 1.6994 | 0.4 | 20000 | 1.6054 | | 1.6726 | 0.6 | 30000 | 1.5718 | | 1.644 | 0.8 | 40000 | 1.5526 ... | c26b258b6d3fffd3a4fee3950e335fa9 |
cc-by-sa-4.0 | ['long-documents'] | false | Citing If you use HAT in your research, please cite: [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint). ``... | dbe6b6e02367a32d7405637590f0bcb2 |
apache-2.0 | ['generated_from_keras_callback'] | false | The-Fanta/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: 0.5162 - Validation Loss: 0.4561 - Train Matthews Correlation: ... | 4c7aa264b48fa9300c731f2dcac26cee |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5162 | 0.4561 | 0.4968 | 0 | | 1eefcd43669661ef731388f6d853888b |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-FF9000 (Deep-Narrow version) T5-Efficient-SMALL-FF9000 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* checkpo... | fdf8f3c19e0627043011d238e649ca72 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-ff9000** - is of model type **Small** with the following variations: - **ff** is **9000** It has **148.6** million parameters and thus requires *ca.* **594.41 MB** of memory in full precision (*fp32*) or **297.2 MB** of memory in half precisio... | 6301d0f6149146db01626e9861377716 |
creativeml-openrail-m | ['text-to-image'] | false | eyyes-diffusion on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 9e9e6944ce76248ddc9eecfda6e74027 |
creativeml-openrail-m | ['text-to-image'] | false | Model by guumaster This your the Stable Diffusion model fine-tuned the eyyes-diffusion concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **00041-2022-11-16, 00038-2022-11-16, 00058-2022-11-16** You can also train your own concepts and upload them to the library ... | 0b17aea2f69d0565ecee9e9498943980 |
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 wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3202 - Precision: 0.5900 - Recall: 0.4118 - F1: 0.4850 - Accuracy: 0.9304 | 2ba78cb8cce8a4c80bf640cafa2db802 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.3469 | 0.5480 | 0.2814 | 0.3718 | 0.9193 | | No log | 2.0 |... | 937712b9aa7b63e20a5759b4db9a4cec |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Whisper-small-ar - Mourad Mars This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.322550 - Wer: 44.976586 | 2e8c3200c00e8ba6d41c15748fda7170 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: train_batch_size=16 eval_batch_size=8 optimizer: Adam learning_rate=1e-5 warmup_steps=500 max_steps=4000 eval_steps=1000 metric_for_best_model="wer" | b7321626fb119adf87484f5c925fa0b9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Step | Validation Loss | Wer | |:-------------:|:----:|:----------------:|:---------:| | 0.2811 | 1000 | 0.393018 | 53.778349 | | 0.2356 | 2000 | 0.348794 | 47.793591 | | 0.1705 | 3000 | 0.332207 | 45.758883 | | 0.1476 | 40... | 8f8c525382ae43d70ebe51d7e4111729 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-IUChatbot-ontologyDts 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: - Loss: 0.2446 | f52f5453e56a06cae7e3410ec8f492f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 382 | 0.2686 | | 0.3946 | 2.0 | 764 | 0.2535 | | 0.2577 | 3.0 | 1146 | 0.2446 | | 4631e85f121fea611f024bd40d22f5f6 |
apache-2.0 | ['summarization'] | false | Hungarian Abstractive Summarization BART model For further models, scripts and details, see [our repository](https://github.com/nytud/neural-models) or [our demo site](https://juniper.nytud.hu/demo/nlp). - BART base model (see Results Table - bold): - Pretrained on Webcorpus 2.0 - Finetuned HI corpus (hvg.hu + i... | e55c34dc77ab874d9c14b81713754cbd |
apache-2.0 | ['summarization'] | false | Results | Model | HI | NOL | | ------------- | ------------- | ------------- | | BART-base-512 | 30.18/13.86/22.92 | 46.48/32.40/39.45 | | BART-base-1024| **31.86/14.59/23.79** | 47.01/32.91/39.97 | | 5a80b42526199de3f759cc88ea7e1479 |
apache-2.0 | ['summarization'] | false | Citation If you use this model, please cite the following paper: ``` @inproceedings {yang-bart, title = {{BARTerezzünk! - Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}}, booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia}, year = {2022}, publisher = {Szegedi Tudomány... | db44889807450d8a8233cbd19ca36530 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-burak-new-300-v2-6 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3074 - Wer: 0.2340 | 09294deb48f2d1558ae7fd0337faa331 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 6.3136 | 9.61 | 500 | 3.1262 | 1.0 | | 1.8247 | 19.23 | 1000 | 0.4049 | 0.5065 | | 0.5387 | 28.83 | 1500 | 0.2828 | 0.346... | a9e315d811af3162961aa1d2272f83cf |
mit | ['spacy', 'token-classification'] | false | testing new lemma dictionaries pip install https://huggingface.co/crodri/ca_bsc_core_trf/resolve/main/ca_bsc_core_trf-any-py3-none-any.whl | Feature | Description | | --- | --- | | **Name** | `ca_bsc_core_trf` | | **Version** | `3.4.5` | | **spaCy** | `>=3.4.1,<3.5.0` | | **Default Pipeline** | `transformer`, `tagge... | ba6c004a726bf6f5aa50e10a0928bb94 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (600 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `ao0cs0`, `ao0fp0`, `ao0fs0`, `ao0mp0`, `ao0ms0... | 8439bd38963dfc87181bfbc016766008 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 97.52 | | `POS_ACC` | 99.24 | | `MORPH_ACC` | 98.96 | | `LEMMA_ACC` | 96.48 | | `DEP_UAS` | 95.25 | | `DEP_LAS` | 93.46 | | `SENTS_P` | 99.71 | | `SENTS_R` | 99.77 | | `SENTS_F` | 99.74 | | `ENTS_F` | 89.77 | | `ENTS_P` | 89.94 | | `ENTS_R` | 89.60 | | `TRANSFORME... | da1f8e9a398f891af28b08d4a8242bee |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-model-sample-27go-emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the go_emotions dataset. It achieves the following results on the evaluation set: - Loss: 4.1765 - Accuracy: 0.5889 | fcbc42729d798da5e33ca5c4a1f162fc |
apache-2.0 | ['generated_from_trainer'] | false | qnli_bert-base-uncased_144 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4604 - Accuracy: 0.9141 | e7fc13b00805d684b7a40a80eaad69ab |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper_small_Korean This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs ko_kr dataset. It achieves the following results on the evaluation set: - Loss: 0.3315 - Wer: 13.0129 | 21fcfe0db8d5c9a4375982c7d371db64 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam wit... | e780a2f92f4c170afed4b533e43e0982 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 35.69 | 500 | 0.3188 | 13.0305 | | 0.0003 | 71.41 | 1000 | 0.3315 | 13.0129 | | a4b355ed1e8e76a14f1179933c5a1abc |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Blot monster model If you like little blot monster, you'd love this fine-tuned SD-1.5 model. Based on some cute little ink blob monsters from some search and trained for only 5000 steps. Use **blotmon** in your prompts. | 1d48b0a7bab7121e6ee8eff57d995968 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Simple samples |  | _0.3, strolling on a garden in blotmon splats_0.3, surreal.png](https://s3.amazonaws.com/moonup/production/uploads/1667159681240-6304f64fce6b12280b1d1d1e.png) | ![00013-1283696413-an illustration... | 4688ec41451bbc2c794c3a4799b2e1c5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Beautiful characters starting from blot monsters |  | ![00017-1620951797... | 9af9cb0ac92057cfdbfc3863f11bbc27 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-gmq-en Neural machine translation model for translating from North Germanic languages (gmq) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in t... | 6c68045ed85a29d45d2de8d1f119db5f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-09 * source language(s): dan fao isl nno nob nor swe * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer... | 4f51610a27b90d58bf7496447b43f5ad |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Han var synligt nervøs.", "Inte ens Tom själv var övertygad." ] model_name = "pytorch-models/opus-mt-tc-big-gmq-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_... | 0941d92254e52686ad8971ed703640ed |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Even Tom was not convinced. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-en") print(pipe("Han var synligt nervøs.")) | 2486e07fd873d361d4ea2c62c8f8079f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 2ebaba5151ce6c1e899602be62e9fe5f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | dan-eng | tatoeba-test-v2021-08-07 | 0.78292 | 65.9 | 10795 | 79684 | | fao-eng | tatoeba-test-v2021-08-07 | 0.47467 | 30.1 | 294 | 1984 | | isl-eng | tatoeba-test-v2021-08-07 | 0.68346 | 53.3 | 2503 | 19788 | | nno-eng | tatoeba-test-v2021-08-07 | 0.697... | 099eaecd74fafb8a6cb949e804d2bc12 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-code-mixed-DS This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7328 - Accuracy: 0.7022 - Precision: 0.6437 - Recall: 0.6634 - F1: 0.6483 | 6bd2337a58250a3b0e10a767e8699596 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 3f92e8f6bbc842ec974546c351822dc0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.098 | 0.5 | 248 | 1.0944 | 0.5352 | 0.2355 | 0.3344 | 0.2397 | | 1.0827 | 1.0 |... | dabff553eb20bb2af12d3120f5bf09ea |
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