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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.0962 | 1.0 | 1012 | 0.7528 | 0.3793 | 0.6109 | 0.4411 | 0.4411 | | 0.7022 | 2.0 |... | 8d7c201cfb9bde9eb6a088e784a9c254 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_no-pretraining_s953 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | 45b36f8dac9d9645b90b6059a73a63b4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-coscan-sex This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9965 | 3cabbe11dae354c24e34536a51abb52d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0034 | 1.0 | 6644 | 0.0229 | 0.9965 | | 1116d4dc3c86c047aaca11007b2764bf |
mit | ['generated_from_trainer'] | false | edos-2023-baseline-roberta-base-label_sexist This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4729 - F1: 0.8048 | 7dbe97cf2d2326b72f5d7b5d17abc50c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4114 | 1.14 | 400 | 0.3516 | 0.7954 | | 0.2725 | 2.29 | 800 | 0.4086 | 0.7925 | | 0.2134 | 3.43 | 1200 | 0.4404 | 0.8062 | |... | 39898724c7a2d7dafedfc28c5a02ac2c |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetunedt 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: 0.0147 - Accuracy: 1.0 | 7daff96ec60a3d2e929334c86b48cedb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4714 | 1.0 | 25 | 0.0147 | 1.0 | | 0.0089 | 2.0 | 50 | 0.0008 | 1.0 | | 0.0101 | 3.0 | 75 | 0.0003 | 1.... | b6809ce8bb0a69994aeb1e3e994a9945 |
apache-2.0 | [] | false | Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base") model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base") ``` | b4939d550cadb60d8f7b07621d0f17d0 |
apache-2.0 | ['generated_from_trainer'] | false | favs-filtersort-multilabel-classification-bert-base-cased This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset. It achieves the following results on the evaluation set: - Loss: 0.3066 - F1: 0.7429 - Roc Auc: 0.8142 - Accuracy: 0.2 | 0daa3de48b2e427bb8f416a486061a07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.7601 | 1.0 | 12 | 0.6966 | 0.2564 | 0.4518 | 0.0 | | 0.6757 | 2.0 | 24 | 0.5629 | 0.6667 ... | e517e5d47e4a40a98cb6487e9a5c25a4 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_English-EWT | Feature | Description | | --- | --- | | **Name** | `en_udv25_englishewt_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | 61281f9b183a4cfc2c888746aec5558e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1760 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `GW... | 9d9941784030528181fae6bf872bd0d3 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.15 | | `TOKEN_P` | 99.18 | | `TOKEN_R` | 99.11 | | `TOKEN_ACC` | 99.83 | | `SENTS_F` | 90.62 | | `SENTS_P` | 90.99 | | `SENTS_R` | 90.26 | | `TAG_ACC` | 96.36 | | `POS_ACC` | 96.94 | | `MORPH_ACC` | 96.91 | | `DEP_UAS` | 91.90 | | `DEP_LAS` | 89.42 | | `LEMMA_A... | e00955f4287807abac2e5366ed7cbd78 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_cooking-roberta-large-v1-5-4 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://w... | e84908186ddae762069e44baa4f51d6c |
mit | ['generated_from_trainer'] | false | training This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the cynthiachan/FeedRef_10pct dataset. It achieves the following results on the evaluation set: - Loss: 0.0810 - Attackid Precision: 1.0 - Attackid Recall: 1.0 - Attackid F1: 1.0 - Attackid Number... | e1f688c67636b739ca3b3b295edb33fd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall ... | bc497eca89ab6baac8ac9ed877e5519c |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Seek.art MEGA is a general use "anything" model that significantly improves on 1.5 across dozens of styles. Created by Coreco at [seek.art](https://seek.art/) This model was trained on nearly 10k high-quality public domain digital artworks with the goal of improving output quality across the board. We find the mod... | cfbac0ff022042c9af780ac315961fe7 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Examples <img src="https://huggingface.co/coreco/seek.art_MEGA/resolve/main/examples.png" style="max-width: 800px;" width="100%"/> The above example images including the prompts and all relevant settings are available [here](https://seek.art/explore/search?collection=6112a64d-bd8b-4043-8d96-88c7cfa65c43). Addition... | 36952d6c8c00eade67297cc3c8af2fd4 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Use Restrictions You agree not to use the Model or Derivatives of the Model: - for the commercial purpose of hosted content generation (inference) without the express written permission of seek.art. Model output for personal use carries no such commercial restriction. - In any way that violates any applicable natio... | 99bf23039f6a4adc1c6eeefafdfb9b48 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3186 - Accuracy: 0.87 - F1: 0.8770 | 6ab7a559704280833d85db28e9d8e1ad |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan | 35fdcc857a46fd63e14ce3e97adad7d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.2575 | 0.47 | 500 | 6.4792 | | 6.4145 | 0.94 | 1000 | 6.4699 | | 6.2252 | 1.4 | 1500 | 6.5489 | | 6.0413 | 1.87 | 2000 | 6.3427 ... | 571ee2d5fad1590493f176cfb32540bc |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.1-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - Rmse: 0.5246 - Mse: 0.2752 - Mae: 0.4184 | bf1db6ce786e714aa85791eab0b52059 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2773 | 0.5266 | 0.2773 | 0.4297 | | 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 ... | 9eeba128cb74a443d75c5a73d4628b10 |
mit | [] | false | Basic use ```python import cv2 import numpy as np import onnxruntime as rt from huggingface_hub import hf_hub_download tagger_model_path = hf_hub_download(repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx") tagger_model = rt.InferenceSession(tagger_model_path, providers=['CUDAExecutionProvider', 'CPUE... | 5a7ad9900be699424bbad62b29fbf6c1 |
mit | [] | false | Multi-gpu batch process ```python import cv2 import torch import os import numpy as np import onnxruntime as rt from huggingface_hub import hf_hub_download from torch.utils.data import DataLoader, Dataset from PIL import Image from tqdm import tqdm from threading import Thread class MyDataset(Dataset): def __i... | 3c195da5f31fbc70bf70d5cde7ab7077 |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets. | e8c814fbe080c367ccaecf4134cf489a |
mit | ['generated_from_keras_callback'] | false | Deep98/Cardinal__Catholicism_-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3075 - Train End Logits Accuracy: 0.8958 - Train Start Log... | 7517080fd13eeff1d9cfbe9a990e711c |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 2fc57774eec76c9db241f4f0cbe13eb9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4795 | 1.28 | 100 | 2.2135 | | 2.0935 | 2.56 | 200 | 2.1722 | | 1.9961 | 3.84 | 300 | 2.1639 | | 1.9455 | 5.13 | 400 | 2.1605 ... | f467718346daa905d424b92185770e5b |
apache-2.0 | ['translation'] | false | alv-eng * source group: Atlantic-Congo languages * target group: English * OPUS readme: [alv-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md) * model: transformer * source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul * t... | 70a90f4759a4aa49d42e52652e02a42b |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ewe-eng.ewe.eng | 6.3 | 0.328 | | Tatoeba-test.ful-eng.ful.eng | 0.4 | 0.108 | | Tatoeba-test.ibo-eng.ibo.eng | 4.5 | 0.196 | | Tatoeba-test.kin-eng.kin.eng | 30.7 | 0.511 | | Tatoeba-test.lin-eng.lin... | c1a18ef877178e69130079b93c2e5714 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: alv-eng - source_languages: alv - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts',... | 3e90861073d525f5d4a901585256010e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | XLS-R-300M - Maltese 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 - MT dataset. It achieves the following results on the evaluation set: - Loss: 0.1895 - Wer: 0.1984 | 4b1b9ee22d1a325107510bbbe70a190b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60.0 - mixed_precisi... | bf172781988ee67a582da6686aa6d150 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4219 | 3.6 | 400 | 3.3127 | 1.0 | | 3.0399 | 7.21 | 800 | 3.0330 | 1.0 | | 1.5756 | 10.81 | 1200 | 0.6108 | 0.5724 | |... | 93e83430d51c802070ff70141786f7c7 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', '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 anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --split test ``` | 2e45083d411f27c17b66f9e6358173bb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', '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 = "anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", split="test", str... | 8be2e1c13bb8cf9547713e045df93636 |
apache-2.0 | [] | false | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | d53e78c4fca89315351c12e19c5f85cb |
apache-2.0 | [] | false | Results on Web Questions - Test Set |Id | link | Exact Match | |---|---|---| |**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wqo**|**40.8**| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wqo|42.8| | 4da0b998a87b6a35139c63787ea5706c |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wqo") t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wqo") input_ids = t5_tok("Whe... | 57b99b1f760a283352e9ff59fbd3fc4e |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-fine-tuned-for-Punctuation-Restoration 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: 0.1097 | 3915932ee702e96cdf8b0162a2910dec |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Base Yue This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 yue dataset. It achieves the following results on the evaluation set: - Loss: 0.3671 - Wer: 69.5864 | 1d001d6268ececb049b2eac726460b05 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - 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_steps: 200 - training_steps: 1000 - mixed_precisi... | 12572fafd413de9304b09512ce34db1e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0998 | 2.78 | 500 | 0.3500 | 71.4517 | | 0.0085 | 5.56 | 1000 | 0.3671 | 69.5864 | | 74f65a675e07800ae4e391c4d0e5a19a |
apache-2.0 | ['translation'] | false | opus-mt-lt-de * source languages: lt * target languages: de * OPUS readme: [lt-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lt-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | 218a7c0a60567760459bd8485a0e3bc6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_model_fine_tuned_unlabeled_all This model is a fine-tuned version of [nouman-10/distilbert_model_fine_tuned_unlabeled_all](https://huggingface.co/nouman-10/distilbert_model_fine_tuned_unlabeled_all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1708 - Accuracy: 0.9... | 3bce88dba3602ecb989d69239e42cd7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1329 | 1.0 | 875 | 0.1708 | 0.95 | | 291e9e4cd6514d9b0b29e64f12143044 |
mit | ['music'] | false | Model description TunesFormer is a Transformer-based melody generation system trained on 285,449 melodies with musical forms (represented by control codes), where all scores are represented in ABC notation. It was introduced in the paper [TunesFormer: Forming Tunes with Control Codes](https://arxiv.org/abs/2301.02884... | a532ef858970707c70dbbb4a3da92596 |
mit | ['music'] | false | Intended uses & limitations You can use this model for melody generation conditioned on musical forms. All scores generated by this model can be written on one stave (for vocal solo or instrumental solo) in standard classical notation, and are in a variety of styles, e.g., blues, classical, folk, jazz, pop, and world... | 1fcaab7caefeb0607f34524ca88b8764 |
mit | ['music'] | false | How to use 1. Install dependencies for the code released in [this repository](https://github.com/sander-wood/tunesformer): ``` torch 1.9.1+cu111 samplings 0.1.7 transformers 4.18.0 ``` 2. Set the `control_codes` and `prompt` in the script `run_inference.py` f... | 692c3c65c37920508412b8de02403398 |
mit | ['music'] | false | Usage ``` optional arguments: -h, --help show this help message and exit -num_tunes NUM_TUNES the number of independently computed returned tunes -max_length MAX_LENGTH integer to define the maximum length in tokens of each tune -top_p TOP_P ... | 1bce271f305012f521c419f09540afe9 |
mit | ['music'] | false | BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2301.02884, doi = {10.48550/ARXIV.2301.02884}, url = {https://arxiv.org/abs/2301.02884}, author = {Wu, Shangda and Sun, Maosong}, keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and informati... | fc9df816fbc5b857c71460b30517c0cd |
apache-2.0 | ['generated_from_trainer'] | false | summarise_v2 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3235 - Rouge2 Precision: 0.018 - Rouge2 Recall: 0.0916 - Rouge2 Fmeasure: 0.0292 | 959d60496a83d2b61654a6fde00edc3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 3.1721 | 0.08 | 10 | 2.7742 | 0.0107 | 0.0671 | 0.0178 ... | d74b89aab792305e535d3979b3f6af07 |
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-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) on the kde4 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2473 - eval_bleu: 41.4902 - eval_runtime: 1405.0341 - eval_samples_per_secon... | 4861981d7281e2e5d51ff90505ae94e7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mk-walkcycle Dreambooth model trained by spooncats 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-stabl... | 5a183d25d02a7576bfa7667c0a842c70 |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Multi2ConvAI-Corona: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type: ... | d2c7a625b9cb82071c553a87ecd8f1ab |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-en-bert")
````
... | 4752cb4598c44ddcf134f8b2609e8cfa |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ka-rina Dreambooth model trained by cdefghijkl 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-stable-di... | 2a2b89fbbb213fb5f8295d8b7c95e393 |
apache-2.0 | [] | false | Graphcore/gpt2-medium-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’... | dd7a1cb3fc8d8ce164e119a9141b6736 |
apache-2.0 | [] | false | Model description GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. ... | b4321bf3c011daad61a5f5a7c85ac4d5 |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the [HuggingFace/gpt2-medium](https://huggingface.co/gpt2-medium) model on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | fff87196f9047ebf37eaca5c7a42266e |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA *Zero-shot SELECTRA* is a [SELECTRA model](https://huggingface.co/Recognai/selectra_small) fine-tuned on the Spanish portion of the [XNLI dataset](https://huggingface.co/datasets/xnli). You can use it with Hugging Face's [Zero-shot pipeline](https://hugging... | ccedcf3c587c11b08532ebf7d03a3226 |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | transformers.ZeroShotClassificationPipeline) to make [zero-shot classifications](https://joeddav.github.io/blog/2020/05/29/ZSL.html). In comparison to our previous zero-shot classifier [based on BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli), zero-shot SELECTRA is **much more lightweight**. As... | 6976af4baaa671bb06ef0669243d827a |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Usage ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="Recognai/zeroshot_selectra_medium") classifier( "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo", candidate_labels=["cultura", "sociedad", "... | 4fa4674d29778507206a9ae567421aad |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Metrics | Model | Params | XNLI (acc) | \*MLSUM (acc) | | --- | --- | --- | --- | | [zs BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli) | 110M | 0.799 | 0.530 | | [zs SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium) | 41M | **0.807** | **0.589** | | zs SELECTRA small ... | 7697252457742a23fef3c982554f336b |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Authors - David Fidalgo ([GitHub](https://github.com/dcfidalgo)) - Daniel Vila ([GitHub](https://github.com/dvsrepo)) - Francisco Aranda ([GitHub](https://github.com/frascuchon)) - Javier Lopez ([GitHub](https://github.com/javispp)) | 516a899c9abba7470c035def205f1fb9 |
apache-2.0 | ['generated_from_trainer'] | false |  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.3650 ... | 583cfe83dd8b92a10ad1b5cdc4a40bc5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | aa5b933f541001e09fd9c5625a46af06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | |:-------------:|:-----:|:---... | b79fcfb9e0bd6a04d76bdbcec22f73a0 |
mit | ['generated_from_keras_callback'] | false | xenergy/gpt2-indo This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3370 - Validation Loss: 1.8387 - Epoch: 0 | a8eb58da60be2d5b96edd9cba7ec3ef2 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | cf62bd1d11aa23930d4378bee1259754 |
apache-2.0 | [] | false | DistilBERT base multilingual model Spanish subset (cased) This model is the Spanish extract of `distilbert-base-multilingual-cased` (https://huggingface.co/distilbert-base-multilingual-cased), a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). This model is cased: it does make a ... | 94d3507a701ae7975be28fa2793c98fa |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-200labels This model is a fine-tuned version of [ogimgio/bert-base-german-cased-finetuned-7labels](https://huggingface.co/ogimgio/bert-base-german-cased-finetuned-7labels) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0744 - Micro f1: 0.0894 -... | bc3688ea91084750f1be603eb08229f7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - 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: constant - num_epochs: 50 | bb86234d42e8ec360b3e1fbbf41c9d16 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.8041 | 1.0 | 1380 | 0.7312 | 0.0422 | 0.0413 | | 0.605 | 2.0 | 2760 | 0.5440 | 0.0436 | 0.0423 | | 0.4... | 395419d467d03bceaed0470ff1dcb43c |
apache-2.0 | ['tapex', 'table-question-answering'] | false | TAPEX-large model fine-tuned on WikiSQL. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Ta... | dbeeae73b0ecd3bb987f1a75da5d08e1 |
apache-2.0 | ['tapex', 'table-question-answering'] | false | define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) | 7f17565bf056a092b4a3c20118959320 |
apache-2.0 | ['translation'] | false | opus-mt-en-ti * source languages: en * target languages: ti * OPUS readme: [en-ti](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ti/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | baf14978f443d3c922d5764339d59d89 |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-224-wi2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3098 - Train Accuracy: 0.9821 - Train Top-5-accuracy: 0.9971 - Validati... | 0db9cca5a50850ef67e4a08a817530a2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 1750, 'end_learning_r... | f641c81ee013a5d760d2ad5f12841050 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-5-accuracy | Validation Loss | Validation Accuracy | Validation Top-5-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 4.4859 | 0.0195 | 0.0579 ... | 326dd20a79b70b132a6f969444539c90 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large v2 Italian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 it dataset. It achieves the following results on the evaluation set: - Loss: 0.1332 - Wer: 4.5576 | 3cdaebb28f1682ebf60252e5bb190e4f |
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: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 2ef0c3b1896ffc951e4d2e4f12149eb3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1684 | 0.17 | 1000 | 0.1620 | 6.4620 | | 0.1174 | 0.33 | 2000 | 0.1418 | 5.5663 | | 0.069 | 1.1 | 3000 | 0.1400 | 5.2865 | |... | e6437d3e71087e0df5bdd2ab9d2aad17 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. | 9b6e9285cfcdc8169a9a67492e7499a3 |
apache-2.0 | ['generated_from_trainer'] | false | amazon_sentiment_sample_of_1900_with_summary 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: 0.1062 - Accuracy: 0.9581 - F1: 0.9579 | 2343cd1c226b692e83887b3ca837e87b |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Description [megagonlabs/t5-base-japanese-web](https://huggingface.co/megagonlabs/t5-base-japanese-web) is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts. Training codes are [available on GitHub](https://github.com/megagonlabs/t5-japanese). The vocabulary size of this model is 32K... | 927df8a0a2d3aa336da9fb6c705383a1 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Corpora We used following corpora for pre-training. - Japanese in [mC4/3.0.1](https://huggingface.co/datasets/mc4) (We used [Tensorflow native format](https://github.com/allenai/allennlp/discussions/5056)) - 87,425,304 pages - 782 GB in TFRecord format - [Japanese](https://www.tensorflow.org/datasets/catalog... | b9cedd1813dacd85a21cb0bbb5a2e39c |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Tokenizer We used Japanese Wikipedia to train [SentencePiece](https://github.com/google/sentencepiece). - Vocabulary size: 32,000 - [Byte-fallback](https://github.com/google/sentencepiece/releases/tag/v0.1.9): Enabled | 800eec45d1cad2077606ab5f34c9157d |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Parameters - T5 model: [models/t5.1.1.base.gin](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/t5/models/gin/models/t5.1.1.base.gin) - Training steps: 1,000,000 It took about 126 hours with TPU v3-8 | 096e5e8b9330f430fa914a6d15eff080 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Related models - [日本語T5事前学習済みモデル (sonoisa/t5-base-japanese)](https://huggingface.co/sonoisa/t5-base-japanese) - [日本語T5事前学習済みモデル (sonoisa/t5-base-japanese-mC4-Wikipedia)](https://huggingface.co/sonoisa/t5-base-japanese-mC4-Wikipedia) | d9d2cc4029519bb199d67087bf43f676 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Citations - mC4 Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ```bibtex @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and... | 522184b88ecaaf474b36820d590d44cf |
mit | ['generated_from_keras_callback'] | false | roberta-base-finetuned-unlabeled_all This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3581 - Validation Loss: 2.1388 - Epoch: 0 | 4e790851d667b143fd86e58dc26a655b |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 62debe2b91af3186d1a518d9d6d7e67f |
apache-2.0 | ['translation'] | false | opus-mt-ts-es * source languages: ts * target languages: es * OPUS readme: [ts-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | ca4d1c50ffedb69c1eb73ec5e40c8798 |
unknown | [] | false | Ella lo dejó como lo dejaban todas, en defensa propia, la dependencia emocional que la había sujetado tantas veces, finalmente se vio superada por su instinto de supervivencia. - Estes una puta, me quieres dejar por ese que te follas cuando discutimos. - Amorcito, yo no estoy con nadie más que contigo y no puedes trat... | 1fd11fad20b412448aaadf50e4905181 |
apache-2.0 | ['translation'] | false | opus-mt-nso-sv * source languages: nso * target languages: sv * OPUS readme: [nso-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 43e0d56edf77bc9de5e789b3dc148992 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.