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 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Ukrainian 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 uk dataset. It achieves the following results on the evaluation set: - Loss: 0.2068 - Wer: 10.0435 | 00000cfc1595a42adb84cd9d6b51a469 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1078 | 1.38 | 1000 | 0.2068 | 10.0435 | | 91fc1c9b680b257d644e036fa4fc3f02 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'telltale', 'game'] | false | Classic Telltale Diffusion This model was trained on arts from gameplay footage across most Telltale classic games, and some game advertisements. The art style can essentially be described as 2D comic arts but in 3D. The model can do portraits, landscapes, and cars, though I have yet to try generating animals. To ref... | 492e2ea88e5c1d4658514169a65de9d3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'telltale', 'game'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Classic_Telltale_Diffusion: [ on the summarize_from_feedback dataset. It achieves the following results on the evaluation set: - Loss: 2.3118 - Rouge1: 30.2401 - Rouge2: 11.4916 - Rougel:... | d8ba893efe46fdbc063b5bc13cc124ec |
apache-2.0 | ['generated_from_trainer'] | false | Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { flan-t5-large-finetuned-openai-summarize_from_feedback (Revision 51666f9) }, year = 2023, url = { https://huggingface.co/mrm8488/flan-t5-large-finetuned-openai-summarize_from_feedback }, doi = {... | 0b1a045da68d9d5f228a48ac65157a78 |
apache-2.0 | ['generated_from_trainer'] | false | vit-large-patch32-384-finetuned-melanoma This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0767 - Accuracy: 0.8273 | c434be69972a0f6c38749e66a8c47f07 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 333fdba1029518c5fe41c0ad2a83e13a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0081 | 1.0 | 550 | 0.7650 | 0.68 | | 0.7527 | 2.0 | 1100 | 0.6693 | 0.7364 | | 0.6234 | 3.0 | 1650 | 0.6127 ... | 59f64700a257b00350c0abae85b3a3af |
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: 0.0609 - Precision: 0.9348 - Recall: 0.9514 - F1: 0.9430 - Accuracy: 0.9864 | 09c23df6ae2e3df6e08676fba31d8747 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0857 | 1.0 | 1756 | 0.0681 | 0.9213 | 0.9337 | 0.9274 | 0.9824 | | 0.0332 | 2.0 |... | ea3b48685a5255a7880d627a1f8bd200 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Chinese texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [Erlangshen-DeBERTa-v2-320M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese). Every word is tagged by [UPOS](https://universald... | 00c2818e0392500f1f968d1b0dc8895b |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-chinese-erlangshen-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-chinese-erlangshen-upos") ``` or ```py import esup... | 6e1f33c14d79f0ed633e5996c24d4704 |
apache-2.0 | ['object-detection', 'vision'] | false | YOLOS (base-sized) model YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](http... | 3ba1b38458697cd884139836681abfb4 |
apache-2.0 | ['object-detection', 'vision'] | false | Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares... | 4a47f256d23b74871bb839a3f78185c8 |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection 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 = YolosFeatureExt... | 2f0447e394eb297aa83b51fc59a12aa6 |
apache-2.0 | ['object-detection', 'vision'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-00666, author = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu... | 42b7243c4d08c100e8c4c87fe9e52b49 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0573 - Accuracy: 0.9848 | 68fee1e85177a87f0abfffebf23fe70e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1564 | 1.0 | 190 | 0.1283 | 0.9737 | | 0.0677 | 2.0 | 380 | 0.0697 | 0.9837 | | 0.0494 | 3.0 | 570 | 0.0573 | 0.... | 93d9fb114fe3d4d5e8378d3292620e25 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_wavlm_s929 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) 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 your speech input is sampled at 1... | 2350b59869c1200b9fd8beec25847d2e |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squadshifts-new_wiki-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://github... | bd5e541a9ce95145069f284c8176aa07 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://gi... | 2ed8483f1d6035c83721c04cf85db1d8 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-squadshifts-new_wik... | c2ef929b77979b77227461d86624132a |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset ... | 9d49e14cbaaa65b7b8338f522f76f791 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - ep... | e9e95a9767d4e6776da05198914f707f |
apache-2.0 | ['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 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 89f64a30a2a46bf236fd2f239dc9e74c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-gn-k1 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 - GN dataset. It achieves the following results on the evaluation set: - Loss: 0.9220 - Wer: 0.6631 | 6e40a01af596ea4beaa2adbac9e809db |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', '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-large-xls-r-300m-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA | 5003ff3161a89c1ec3cc17838810fbd3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00018 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | fee6f9d650e9b2f794e84f899813bb50 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'gn', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 15.9402 | 8.32 | 100 | 6.9185 | 1.0 | | 4.6367 | 16.64 | 200 | 3.7416 | 1.0 | | 3.4337 | 24.96 | 300 | 3.2581 | 1.0 ... | ee2c7eea4b6c63baa5ed4dc15f678504 |
mit | ['generated_from_trainer'] | false | nifty_thompson This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekko... | a359e5413e8df5be6f0606e9ae1d9de2 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | cbb0b187cc3cab45f07baa2ae4d6f6fb |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1754 - F1: 0.8440 | 1c2467ec24a762efa79e55c860a75aac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3536 | 1.0 | 394 | 0.2111 | 0.7964 | | 0.1759 | 2.0 | 788 | 0.1786 | 0.8331 | | 0.1126 | 3.0 | 1182 | 0.1754 | 0.8440 | ... | 85b6232084dd50860312f39ec7459f85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Georgian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 8f7cc1408c4a5ae5d0fffd1ad34c209b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer ``` **Normalizer** ```bash !wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lith... | bda8d6b3422e4abb1440a587a8837b51 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Georgian test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import re import string from normalizer im... | a7ef3b9f69bb1cd071091a7ddb2e2751 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training & Report The Common Voice `train`, `validation` datasets were used for training. You can see the training states [here](https://wandb.ai/m3hrdadfi/wav2vec2_large_xlsr_ka/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Georgian--Vmlldzo1OTQyMzk?accessToken=ytf7jseje66a3byuheh68o6a7215thjviscv5k2ewl5hgq9yqr50yx... | c5335514bd7640ad5ecf43f9e5c1213c |
apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](... | f3bedd1e1ad9a0990603aa298b6bba77 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model provided by: DarkDude31 Fine-tuned (from misc) halvany\_oszi\_rozsa model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | 9e7bf36aba9c655d5f179fe17d41cb03 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | How to use You can generate music from this fine-tuned (from misc) halvany_oszi_rozsa model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r). Only the `gen_ema.h5` file is needed to generate music. Place it in your `checkpoints` folder. | 96897d7711c2f4db1eec7f3a867803fa |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout e08a89e0a43db7fc12bec835c62a000ad10bd417 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_mini_librispeech_diar ``` <!-- Generated by scripts/utils/show_diar_result.sh --> | cb1bd2164fcf1eeb3b579b09c0f97cea |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Environments - date: `Tue Feb 8 16:41:16 KST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1` - Git hash: `e08a89e0a43db7fc12bec835c62a000ad10bd417` - Commit date: `Sun Feb 6 18:54:20 2022 -0500` | 3fcc01b0b56afc14c9693ed894bd4d5a |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DER dev_clean_2_ns2_beta2_500 |threshold_median_collar|DER| |---|---| |result_th0.3_med11_collar0.0|31.39| |result_th0.3_med1_collar0.0|31.78| |result_th0.4_med11_collar0.0|29.99| |result_th0.4_med1_collar0.0|30.61| |result_th0.5_med11_collar0.0|29.28| |result_th0.5_med1_collar0.0|30.19| |result_th0.6_med11_collar0.0|... | 861f4d08f79651573109764d8932e5c3 |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DIAR config <details><summary>expand</summary> ``` config: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null ... | 133cb462854b331a85cce4a39eeaf385 |
apache-2.0 | ['Quality Estimation', 'microtransquest'] | false | Using Pre-trained Models ```python from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel import torch model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_zh-wiki", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available()) source_tags, target_tags = model.predict(... | 706fcf188f9dc6f93636ddc4d1d91137 |
apache-2.0 | ['translation'] | false | opus-mt-fi-sv * source languages: fi * target languages: sv * OPUS readme: [fi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sv/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-04-11.zip](ht... | 56e6f5115290f52a017546b9956122dc |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6837 - Matthews Correlation: 0.1055 | 3e7654094aef78c44d730bd87de1c66b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.6247 | 1.0 | 1669 | 0.6837 | 0.1055 | | 0.5458 | 2.0 | 3338 | 0.7216 | 0.1168 | |... | 6d3fcc97cd6c56be4c2511ecec6be7ad |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-Noah_Titan-4200 được train bởi hr16 bằng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bằng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVm... | f863fd1a857de3afa69ec09fea201631 |
apache-2.0 | ['splinter', 'SplinterModel'] | false | Splinter large model, (with pretrained QASS-layer weights) Splinter-large is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/spli... | df02b7b1dd81c41c26f18e6fdf96379e |
apache-2.0 | ['splinter', 'SplinterModel'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ram-etal-2021-shot, title = "Few-Shot Question Answering by Pretraining Span Selection", author = "Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer", booktitle = "Proceedings of the 59th... | 6bb5674ba5ffa790a2e65ea764910cc3 |
mit | ['generated_from_trainer'] | false | hasoc19-microsoft-mdeberta-v3-base-HatredStatement-new This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6039 - Accuracy: 0.7329 - Precision: 0.7324 - Recall: 0.7... | 4ca61a29dd124fe8234fb78d881e8502 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5276 | 0.7253 | 0.7258 | 0.7253 | 0.7225 | | 0.5406 | 2.0 |... | b84b9d7b8be92c668f5179311237b65a |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mbart-large-cc25-koquad-qg` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/... | 552ad034351775ca072fd8476488fd30 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc2... | 6e48bd3096438a8d995ab37c0e5e8725 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | ... | a91e642ce44f3a4f1454d528f7328562 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 84e194d5dc7cac81b9d22ee0e8f4ccb5 |
apache-2.0 | ['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: 1.0 | b9f7081ab2705a96703f4181ae521832 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-gc-indep This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1014 - Accuracy: 0.983 - F1: 0.9746 | a20ae0832c56185769b9621815acd011 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2611 | 1.0 | 32 | 0.1014 | 0.983 | 0.9746 | | c52b65daed0d11cfde40fb0b6c549d3a |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-toi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1668 - Wer: 63.5938 | ea6d5fbc184584561297523f610632aa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precisi... | 650d920af925721dec97ad6d952c6caa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.568 | 1.47 | 500 | 2.1883 | 72.0402 | | 0.2614 | 2.95 | 1000 | 2.1071 | 67.1034 | | 0.0811 | 4.42 | 1500 | 2.3456 | 6... | c76f08822790621e83ba0180df065963 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_300k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 300k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 8dadbd074490d048fa930c13ee5d56c7 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_300k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_300k') model = TFBertModel.from_pretrained("google/multibe... | 323c70d725d068bab900f7cd6e3c1613 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-viquad This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9815 | d9f4223e35b78afc0b7b0c9ee52acf49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 65 | 2.5534 | | No log | 2.0 | 130 | 2.1165 | | No log | 3.0 | 195 | 1.9815 | | 94a402a9e3a5b168fa901f2a6bed534d |
apache-2.0 | ['translation'] | false | ine-eng * source group: Indo-European languages * target group: English * OPUS readme: [ine-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md) * model: transformer * source language(s): afr aln ang_Latn arg asm ast awa bel bel_Latn ben bho bos_Latn bre bul bul_Latn cat c... | 97b6312db9e409b7ee0819880fa3d16a |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014-hineng.hin.eng | 11.2 | 0.375 | | newsdev2016-enro-roneng.ron.eng | 35.5 | 0.614 | | newsdev2017-enlv-laveng.lav.eng | 25.1 | 0.542 | | newsdev2019-engu-gujeng.guj.eng | 16.0 | 0.420 | | newsdev2019-en... | f0bb0bf7ec3170f8a76f6554cc93bdaf |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ine-eng - source_languages: ine - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'es', 'os', 'ro', 'fy', 'cy', 'sc', 'is', 'yi',... | 4a9a4959e7a6f3d7da3cf0dd1231cdc5 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Univer... | befab40ea1fa7fa443b5972ec49931a6 |
apache-2.0 | ['chinese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos") ``` or ```py import esupar nlp=esupar.load("Koic... | 6147d85507761b32b01b23b5e6fb815f |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Card for 🧑🏻🚀COSMO 🧑🏻🚀COSMO is a conversation agent with greater generalizability on both in- and out-of-domain chitchat datasets (e.g., DailyDialog, BlendedSkillTalk). It is trained on two datasets: SODA and ProsocialDialog. COSMO is especially aiming to model natural human conversations. It can accept ... | 67262498d8c2f8dbe4639f6374576e63 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Description - **Repository:** [Code](https://github.com/skywalker023/sodaverse) - **Paper:** [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465) - **Point of Contact:** [Hyunwoo Kim](mailto:hyunwook@allenai.org) | 71c622481961f777073d6a0606348f95 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Model Training 🧑🏻🚀COSMO is trained on our two recent datasets: 🥤[SODA](https://huggingface.co/datasets/allenai/soda) and [Prosocial Dialog](https://huggingface.co/datasets/allenai/prosocial-dialog). The backbone model of COSMO is the [lm-adapted T5](https://huggingface.co/google/t5-xl-lm-adapt). | d9763db571b16cc0b64152e8cffd4b86 |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | How to use > 💡 <b>Note:</b> The HuggingFace inference API for Cosmo is not working correctly, we gently guide you to [our repository](https://hyunw.kim/sodaverse) to try out the demo code! > 🚨 <b>Disclaimer:</b> We would like to emphasize that COSMO is trained on SODA and ProsocialDialog mainly for academic/resear... | eba1c7fcb9342fd8ed0533aee029c37f |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Further Details, Social Impacts, Bias, and Limitations Please refer to our [paper](https://arxiv.org/abs/2212.10465). Cosmo is mostly trained on social chitchat. Therefore, we do not encourage having knowledge-intensive conversations (e.g., science, medical issues, law). Significant research has explored bias and fair... | 611ff0017ea946da36a611309a851dde |
apache-2.0 | ['conversational', 'dialogue', 'response generation'] | false | Citation Please cite our work if you find the resources in this repository useful: ``` @article{kim2022soda, title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization}, author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zho... | 234c847c2054f1c54beff580569fe862 |
apache-2.0 | ['generated_from_trainer'] | false | bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2559 - Precision: 0.7221 - Recall: 0.7242 - Fscore: 0.7223 | e7f3f216e5184d6f1c0e19e33a76f9bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8588 | 1.0 | 815 | 0.8342 | 0.7807 | 0.6117 | 0.6364 | | 0.5394 | 2.0 | 1630 | 0.9126 | 0.7363 ... | 7ba00996c86e346c865ff563eb2d42fa |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT', 'RobBERTje'] | false | The models | Model | Description | Parameters | Training size | Huggingface id | |--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| | Non-sh... | 24c9191fd72f8fb8750d010cb8824ac8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Chinese-zh-CN-aishell1 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the [AISHELL-1](https://github.com/kaldi-asr/kaldi/tree/master/egs/aishell) dataset. When using this model, make sure that your speech input is sampled at... | b44f4cac1f7513abb169f405a0c775f7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import librosa from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor device = "cuda:0" if torch.cuda.is_available() else "cpu" processor = Wav2Vec2Processor.from_pretrained( 'qinyue/wav2vec2-large-xlsr-53-ch... | 4bd49f2e3f9976c0334dde46261d986f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation ```python wer_metric = load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids, spaces_between_special_toke... | 70ffa01b9a302f15fd0b17737914a18b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Results | Reference | Prediction | | ------------- | ------------- | | 据 伟 业 我 爱 我 家 市 场 研 究 院 测 算 | 据 北 业 我 爱 我 家 市 场 研 究 院 测 算 | | 七 月 北 京 公 积 金 贷 款 成 交 量 提 升 了 百 分 之 五 | 七 月 北 京 公 积 金 贷 款 成 交 量 提 升 了 百 分 之 五 | | 培 育 门 类 丰 富 层 次 齐 用 的 综 合 利 用 产 业 | 培 育 门 类 丰 富 层 资 集 业 的 综 合 利 用 产 业 | | 我 们 迎 来 了 赶 超 发 达 国 家 的 难 得 ... | 88e1ea1068abea26f9964e6816990184 |
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: 0.3213 - Accuracy: 0.8667 - F1: 0.8684 | 2a8fce11ce73f1381a5be163edb2aeca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 | 796644f7451862ba7f5955e72d5eae1e |
apache-2.0 | [] | false | _Copyright 2023 Anugrah Akbar Praramadhan. All rights reserved._ _Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at_ _[http://www.apache.org/licenses/LICENSE-2.0)](http://www.apache.org/licenses/LICE... | 0357d1b368f13cac18e34f3cb6fee574 |
apache-2.0 | [] | false | Model Description A GPT-2 *(Generative Pretrained Transformer-2)* model is a transformer based architecture for Causal Language Modeling, meaning it's required a left token/word as an input prompt for generating the right/next token, developed by Open AI *{Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David a... | cca0c8a93d08292f12b6b6ff0150c28e |
apache-2.0 | [] | false | Limitation Since GPT-2 is an unsupervised model and trained using an unlabelled of text sequences without any explicit supervision, the clarity and output of this model often comes with randomness. To overcome this issue we have to create a specific seed for determined output. Supported language for this model is onl... | 7b1abe26bffefd6e291d1022eded5147 |
apache-2.0 | [] | false | How To Use Direct use of using Pytorch: ```python >>> from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, set_seed >>> model_name = 'anugrahap/gpt2-indo-textgen' >>> tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left') >>> model = AutoModelForCausalLM.from_pretrained(model_n... | 03bf36fc158a724a8d450be08a945b5a |
apache-2.0 | [] | false | Learn more | [GPT-2 Pretrained Model Medium-345M Parameters](https://github.com/openai/gpt-2/blob/master/download_model.py)<br> | [Indo4B Wikipedia CoNLL-U Dataset - 433MB by IndoNLP](https://drive.google.com/file/d/1ZoKd31yr3soveU0O38XEIFUBKx-D66t5/view?usp=sharing)<br> | [References for CoNLL-U format](https://univ... | 867c1f3ce67ee9b3ccf5b7907c5e2893 |
apache-2.0 | ['generated_from_keras_callback'] | false | tfranklin/bert-a-saurus 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: 0.0003 - Validation Loss: 0.0004 - Epoch: 2 | 9028785b06e1bc3acc7083e1438f665d |
apache-2.0 | ['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... | f4f2d6e50bb1b8d3f3e4d70009790292 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2424 | 0.0004 | 0 | | 0.0004 | 0.0004 | 1 | | 0.0003 | 0.0004 | 2 | | c1c1f872b94907d2f17335adb35c8d5c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | {INSTANCE_NAME} Dreambooth model trained by zuruyu with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get start... | 0ecf0b07d61481727d89bb952e0616ab |
apache-2.0 | [] | false | This is a pretrained [MT5](https://github.com/google-research/multilingual-t5) large model (**973M** parameters). Training was performed with the span corruption task on a clean 80GB Romanian text corpus for 4M total steps with these [scripts](https://github.com/dumitrescustefan/t5x_models), starting from the 1M publ... | 284ef9f449447402f0fbece34c3a2fea |
apache-2.0 | [] | false | How to load an mt5x model ```python from transformers import MT5Model, T5Tokenizer model = MT5Model.from_pretrained('dumitrescustefan/mt5-large-romanian') tokenizer = T5Tokenizer.from_pretrained('dumitrescustefan/mt5-large-romanian') input_text = "Acesta este un test." target_text = "Acesta este" inputs = tokenizer(... | 8b0807bdd8a211e8eaeec46ee91970f6 |
apache-2.0 | [] | false | this will print [1, 4, 1024] ``` Remember to always sanitize your text! Replace ``ş`` and ``ţ`` cedilla-letters to comma-letters with : ```python text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") ``` because the model was **not** trained on cedilla ``ş`` and ``ţ``s. If you don't, you... | 71fcdf84ad1f14cda9d95dd155e5ee6f |
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