license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 10 | 0.6399 | 0.0 | 0.0 | 0.0 | 0.6603 | | No log | 2.0 | 20 | 0... | e4f85505e83ee37974e78b45eb6cf57e |
apache-2.0 | [] | false | Spanish Bert2Bert fine-tuned on Quora question pairs dataset Fine-tuning of a [question generator model](https://huggingface.co/mrm8488/bert2bert-spanish-question-generation) into a paraphraser model using a poor-man's translation of the Quora question pairs dataset. It basically rephrases questions into similar ques... | de768ac16c99dde122127664110101c6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2520 | c9e0850544a197e268a68a73e8a3dc5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0949 | 1.0 | 291 | 1.7072 | | 1.649 | 2.0 | 582 | 1.4409 | | 1.4835 | 3.0 | 873 | 1.4099 | | 1.3938 | 4.0 | 1164 | 1.3858 ... | dadb7e96f8c331f5fdb12013b9ed7212 |
cc-by-4.0 | [] | false | Overview **Language model:** bert-base-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Infrastructure**: 1x Tesla v100 | 225b730fdccc241b43026a61e6c8869f |
cc-by-4.0 | [] | false | Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "bert-base-uncased" max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` | 12bdb04f32d53f3e014e51cb12f6957f |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'ilpost', 'summarization'] | false | mT5 Small for News Summarization ✂️🗞️ 🇮🇹 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on news summarization on the [Fanpage](https://huggingface.co/datasets/ARTeLab/fanpage) and [Il Post](https://huggingface.co/datasets/ARTeLab/ilpost) corpor... | dd234d96e33bfaf5e3d34b127f2025d9 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'ilpost', 'summarization'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines newsum = pipeline("summarization", model='it5/mt5-small-news-summarization') newsum("Dal 31 maggio è infine partita la piattaforma ITsAR... | e5d19f49cb0f881ea5b60d974e2f027a |
apache-2.0 | ['translation'] | false | eng-roa * source group: English * target group: Romance languages * OPUS readme: [eng-roa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-roa/README.md) * model: transformer * source language(s): eng * target language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad... | d73fa6ca7dd338b93094f481d72adbc3 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-enro-engron.eng.ron | 27.6 | 0.567 | | newsdiscussdev2015-enfr-engfra.eng.fra | 30.2 | 0.575 | | newsdiscusstest2015-enfr-engfra.eng.fra | 35.5 | 0.612 | | newssyscomb2009-engfra.eng.fra | 27.9 | 0.570... | 6004f5c91b07789190d35408ee4045ba |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-roa - source_languages: eng - target_languages: roa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-roa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'it', 'ca', 'rm', 'es', 'ro', 'gl', 'co', 'wa',... | 41a0dc130c7092a34387b124f4aba8fb |
mit | ['generated_from_trainer'] | false | spelling-correction-english-base-location-unique-2-2-proportional This model is a fine-tuned version of [grantslewis/spelling-correction-english-base-location-unique-2-2](https://huggingface.co/grantslewis/spelling-correction-english-base-location-unique-2-2) on the None dataset. It achieves the following results on ... | 632640985050cf5daffdae6dbb5acdd0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 75 - eval_batch_size: 75 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 38a75d48d0ce7623830a0c929895647e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.097 | 1.0 | 5659 | 0.0771 | 0.0183 | | b9bb9d972019abe5eaf1d27700bb09c2 |
apache-2.0 | [] | false | Model description
The REALM checkpoint finetuned with WebQuestions(WQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
| aeded0d00d7e64faf69160444a56ec5a |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout ec1acec03d109f06d829b80862e0388f7234d0d1 pip install -e . cd egs2/wsj0_2mix/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave ``` <!-- Generated by ./scripts/utils/show_enh_s... | 7e36e7264a75daacb99e63cdb76b8863 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Environments - date: `Thu Mar 3 14:33:32 CST 2022` - python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.5.1+cu101` - Git hash: `ec1acec03d109f06d829b80862e0388f7234d0d1` - Commit date: `Fri Feb 25 14:12:45 2022 +0800` | e4a16db7fdf60e403d5e1068b32eee54 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | .. config: conf/tuning/train_enh_dan_tf.yaml |dataset|PESQ|STOI|SAR|SDR|SIR|SI_SNR| |---|---|---|---|---|---|---| |enhanced_cv_min_8k|2.68|0.88|12.28|11.01|18.03|10.48| |enhanced_tt_min_8k|2.68|0.89|12.10|10.84|17.98|10.30| | 4c5936e776d968e25606cd2d2ada57f5 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_dan_tf.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_dan_tf_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null... | cec3bdfec9c76316e5b701efa15bd023 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | 8ee52af95cb3cb6a01d21325653a0bb0 |
apache-2.0 | ['generated_from_trainer'] | false | model1-thesis-3 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: 1.1377 - Precision: 0.4527 - Recall: 0.5051 - F1: 0.4774 - Accuracy: 0.6190 | 937e31fe4bb132677c1cfb63df44b4ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 45 | 1.3105 | 0.3737 | 0.4765 | 0.4189 | 0.5364 | | No log | 2.0 |... | a11cd5887b51dcb38c246730cc94b4c8 |
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: 0.1339 - F1: 0.8663 | e4221ae518063493fa8d7261656a337f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2581 | 1.0 | 525 | 0.1690 | 0.8303 | | 0.1305 | 2.0 | 1050 | 0.1352 | 0.8484 | | 0.0839 | 3.0 | 1575 | 0.1339 | 0.8663 | ... | 75254f4f2259fa0d7e716f9bf705c073 |
mit | ['generated_from_trainer'] | false | job-listing-filtering-model This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1992 | a8e438d93df6075281c46508df90f09d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | fc736aaa6068cde5c183bf65b91ca337 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4639 | 1.55 | 50 | 0.4343 | | 0.407 | 3.12 | 100 | 0.3589 | | 0.3459 | 4.68 | 150 | 0.3110 | | 0.2871 | 6.25 | 200 | 0.2604 ... | f895482677b0ed3afbef7ab8662c0eed |
apache-2.0 | ['translation'] | false | opus-mt-efi-sv * source languages: efi * target languages: sv * OPUS readme: [efi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/efi-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | beb492aa5cf9ca9368d256b657114465 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-bert-large-uncase-p1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0993 | 926dfd29fc66c08e4f8f16fe83d2bc56 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-16-6 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: 0.8356 - Accuracy: 0.6480 | aa5bf2372663e472f2e7742a1629e765 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6978 | 1.0 | 7 | 0.6807 | 0.4286 | | 0.6482 | 2.0 | 14 | 0.6775 | 0.4286 | | 0.6051 | 3.0 | 21 | 0.6623 | 0.... | 00d25b593805de3366f8d8fb313f8b36 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-da-multiwoz_fs0.05 This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3984 - Accuracy: 37.0884 - Num: 367 - Gen Len: 15.5232 | d3294e6fe231b13c19396d7ae899b90f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:-------:| | 1.734 | 0.56 | 100 | 0.6414 | 20.7578 | 367 | 12.6294 | | 0.7022 | 1.12 | 200 | 0.4979 | 28.9542 | 367 |... | 01e31b05b30ed48f0cf3573b6f562bd9 |
mit | [] | false | model by akolov This your the Stable Diffusion model fine-tuned the Vasko style second try concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a painting by vasko style** You can also train your own concepts and upload them to the library by using [this notebook](h... | de3a45b3d8425de9282d4199d273a4b1 |
mit | [] | false | orientalist art on Stable Diffusion This is the `<orientalist-art>` 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. Yo... | 57ca979c895a352394484784b81b5897 |
mit | ['generated_from_keras_callback'] | false | MariaZafar/gpt2-finetuned-wikitext2 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: 0.7785 - Validation Loss: 3.7004 - Epoch: 49 | d60c94eedd9c71a7acaee16f34cb6f34 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.8858 | 7.5655 | 0 | | 4.0619 | 5.8193 | 1 | | 3.3766 | 4.9585 | 2 | | 3.0686 | 4.5764 | 3 | | 2.9022 | 4.3847 | 4 | | 2.7838 |... | 3420fbdb2035db0213d12509341bb22f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | 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-diffusion/blob/main/fast_stable_diffusion_AUTO... | a738d79a08301113923e60cf597e54d9 |
mit | [] | false | German BERT base fine-tuned to predict educational requirements This is a fine-tuned version of the German BERT base language model [deepset/gbert-base](https://huggingface.co/deepset/gbert-base). The multilabel task this model was trained on was to predict education requirements from job ad texts. The dataset used f... | 3f6e8813e5cf41cfcf0f7d9dde5170f2 |
mit | [] | false | label-ranking-average-precision) was tracked for the testing set. LRAP measures what fraction of higher-ranked labels produced by the model were true labels. To account for the label imbalance, the rankings were weighted so that improperly ranked rare labels are penalized more than their more frequent counterparts. Aft... | 94f28e83ef66ca82852c6a175ae24950 |
mit | [] | false | See also: - [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) - [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) - [gonzpen/gbert-large-ft-edu-redux](https://huggingface.co/gonzpen/gbert-large-ft-edu-redux) | c7f8024f43a51bfc91c1da15835c2e97 |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['Frédéric_Chopin', 'Prime_minister', 'Arnold_Schwarzenegger', 'Alexander_Graham_Bell', 'Virgil', 'Mary_(mother_of_Jesus)', 'John,_King_of_England', 'Athanasius_of_Alexandria', 'Bill_%26_Melinda_Gates_Foundation', 'Edmund_Burke', 'Pope_Paul_V... | 07bfe4d03485541aead89e6a52fe9bb6 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-detox This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1819 | 6dd613fbfad41f636c4b02ee1ad379c6 |
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: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 055c07c5939861e87050106d84559003 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5633 | 1.0 | 135 | 0.2524 | | 0.2589 | 2.0 | 270 | 0.2193 | | 0.2307 | 3.0 | 405 | 0.1993 | | 0.2171 | 4.0 | 540 | 0.2002 ... | d0bcfd0ceaeefb9b399758a194411e89 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-MIR_ST500-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7360 - Wer: 0.9837 | 4af226558ccadf4c7b03b4446728c1e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 500 - mixed_precision... | 6e28b07da5744594b1a204e936e8ea62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 101.0917 | 16.67 | 100 | 18.8979 | 0.8208 | | 15.5054 | 33.33 | 200 | 10.9184 | 0.8208 | | 10.1879 | 50.0 | 300 | 7.6480 | 0.820... | d53bde9137afa60ce2dc359c838f3d51 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-NER-favsbot This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1680 - Precision: 0.8462 - Recall: 0.88 - F1: 0.8627 - Accuracy: 0.9444 | e9c864f724f4225fd1466082c6f96ce5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 7 | 1.8761 | 0.0 | 0.0 | 0.0 | 0.5833 | | No log | 2.0 |... | 73b9a0cd494236bb457505fb4f4e06e0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the EXPERIMENTS/DATA/ATCOSIM_UWB_ATCC/TRAIN - NA dataset. It achieves the following results on the evaluation set: | 16238623de59542a49f4cfba228729cf |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on two corpus: - [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc), and - [ATCOSIM corpus](https://... | 3634315920fd4269617d44d348d914e3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | Training and evaluation data See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. - We use the UWB-ATCC + ATCOSIM... | 11b860b04a09c7a7a93f86528047ff3e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | Writing your own inference script If you use language model, you need to install the KenLM bindings with: ```bash conda activate your_environment pip install https://github.com/kpu/kenlm/archive/master.zip ``` The snippet of code: ```python from datasets import load_dataset, load_metric, Audio import torch from tr... | ed5fa89a75947e4c66e65b5e26b9d443 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | 3. Load the processors, we offer support with LM, which should yield better resutls if USE_LM: processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) else: processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) | d40c8d0992b250b0c9eae6e81283541d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | resample if neccessary if file_sampling_rate != 16000: resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() else: resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() input_values = processor(resampled_audio, return_tensors="pt").input_values | fa1b7b0d0bb72a9127d1d68f83241443 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | get the transcription with processor if USE_LM: transcription = processor.batch_decode(logits.numpy()).text else: pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) | d24fd36f31bdb99b7e1f2a58e6649d32 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iulii... | 6eff4a9953e5d7b849575f429ce42892 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 - mixed_pre... | d77e3f7bf171d5636bb5fb9b60684475 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en-atc', 'en', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.63 | 500 | 2.2638 | 0.9359 | | 2.6089 | 1.27 | 1000 | 0.7277 | 0.2407 | | 2.6089 | 1.9 | 1500 | 0.5800 | 0.174... | 9d65e044c6216d155e33d9ad516a28d4 |
other | [] | false | This is the model trained for this short video: https://www.youtube.com/shorts/hpIUKRTopAY This AI generates Christmas gift ideas. This model was trained on a small dataset webscraped from the Toys-R-Us website. This dataset consisted of search terms and the names of the best selling items corresponding to said se... | 1206299306769a39e7060f35e63fd175 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese [Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset plus a data augmentation method based on TTS and voice con... | 6aae66191ff16faae7824106f79a8b64 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Use this model ```python from transformers import AutoTokenizer, Wav2Vec2ForCTC tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese") model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-d... | 3139502a9966828d74b61363a805f830 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Example test with Common Voice Dataset ```python dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-7.0-2021-07-21") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"... | cbb9941b948c901987ef05d3ea85c510 |
other | ['generated_from_trainer'] | false | 6.7b-dalio-book-handwritten-io-constant-1e-6 This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted dataset. It achieves the following results on the evaluation set: - Loss: 2.3633 - Accuracy: 0.3103 | 98258c2292c8ef73fe33301eb4a983ac |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6396 | 0.11 | 6 | 2.5039 | 0.2989 | | 2.5754 | 0.21 | 12 | 2.4902 | 0.2999 | | 2.5859 | 0.32 | 18 | 2.4648 | 0.... | c149e7894a8a632b22a07bf443c2e091 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/XXX08_02_23__-bucket-finetunned This model is a fine-tuned version of [kasrahabib/after_training_rus_combined_relabeled_data_from-bucket-finetunned_batch_size_16](https://huggingface.co/kasrahabib/after_training_rus_combined_relabeled_data_from-bucket-finetunned_batch_size_16) on an unknown dataset. It ach... | ce33a65d7f12792d661898379f48da0c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8010, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 6390bbb21cda244a94fa82368e485578 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3976 | 0.3499 | 0 | | 0.2199 | 0.3588 | 1 | | 0.1392 | 0.3404 | 2 | | 0.0962 | 0.3372 | 3 | | 0.0684 | 0.3182 | 4 | | 0.0595 |... | 0cc3b26f6ca7dacebaa121bb086001b0 |
mit | [] | false | ransom on Stable Diffusion This is the `<ransom>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train y... | 108a2465fa632cbe8e77ae69e0f0996f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_toy_train_data_masked_audio This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6445 - Wer: 0.4938 | 35daa2dec912c819de24e634774ca108 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3761 | 1.05 | 250 | 3.4022 | 0.9954 | | 3.0858 | 2.1 | 500 | 3.4684 | 0.9954 | | 2.6302 | 3.15 | 750 | 1.7989 | 0.9865 | |... | d65745682d897c8179c6d8e88de9b0e4 |
mit | [] | false | model by DavLeonardo This your the Stable Diffusion model fine-tuned the sofi concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sofi** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/g... | 78f7a26fb366ff5959aa334337b155cc |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-squad_v2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3226 | b5261c4ae80fa6387df1c0c7070d3782 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.03 | 1.0 | 8255 | 1.1334 | | 0.7511 | 2.0 | 16510 | 1.1299 | | 0.5376 | 3.0 | 24765 | 1.3226 | | d44f6fc1144a662439c7ea9f4ed551c1 |
creativeml-openrail-m | ['text-to-image'] | false | alisks Dreambooth model trained by HusseinHE with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/bl... | b2f8c0161bb718f12b9885161766f6e5 |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. RWKV-4 1.5B is out: https://huggingface.co/BlinkDL/rwkv-4-pile-1b5 At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it. ctx_l... | 401e545a9e5cc442da5d6e5d40410759 |
apache-2.0 | ['ZEN', 'chinese'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | ZEN1 | 224M | 中文-Chinese | | 16d0f7b6a4c08caa1608fcb410ec9c09 |
apache-2.0 | ['ZEN', 'chinese'] | false | 模型信息 Model Information 我们与[ZEN团队](https://github.com/sinovation/ZEN)合作，使用我们的封神框架，开源发布了ZEN1模型。具体而言，通过引入无监督学习中提取的知识，ZEN通过N-gram方法学习不同的文本粒度信息。ZEN1可以通过仅在单个小语料库(低资源场景)上进行训练来获得良好的性能增益。下一步，我们将继续与ZEN团队一起探索PLM的优化，并提高下游任务的性能 We open source and publicly release ZEN1 using our Fengshen Framework in collaboration with the [ZEN t... | 99089505614569447d2a3cb2f7b280d7 |
apache-2.0 | ['ZEN', 'chinese'] | false | 下游效果 Performance **分类任务 Classification** | model | dataset | Acc | | ---- | ---- | ---- | | IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese | Tnews | 56.82% | **抽取任务 Extraction** | model | dataset | F1 | | ---- | ---- | ---- | | IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese | OntoNote4.0 | 80.8% | | 911db443bbdaa76beb584ba7ad3314ac |
apache-2.0 | ['ZEN', 'chinese'] | false | 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有ZEN1相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of ZEN1 in [transformers library](https://github.com/huggingface/transformers), you can find the structure of ZEN1 and... | bc29d3fb78b97125e6fe83e087f4a794 |
apache-2.0 | ['ZEN', 'chinese'] | false | 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文： If you are using the resource for your work, please cite the our paper for this model: ```text @inproceedings{diao-etal-2020-zen, title = "ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations", author = "Diao, Shizhe and Bai, Jiaxin and Song... | 63fccf13296c7da477ad8c57e2566179 |
creativeml-openrail-m | [] | false | utsurome_v2.safetensors [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/utsurome_v2.png" width="512" height="768">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/utsurome_v2.png) <details> <summary>Sample Prompt</summary> <pre> masterpiece, best qual... | 418c35a64c18290ffdb4c46ae1b036c6 |
creativeml-openrail-m | [] | false | yorime.safetensors [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/yorime.png" width="512" height="768">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/yorime.png) <details> <summary>Sample Prompt</summary> <pre> masterpiece, best quality, 1girl, emp... | 52ccea5cf0b670a0ec7e0fcbcd402428 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_wavlm_s455 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | 3bdaf9d83f880f50c86f0480f347b1bd |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-financial-phrasebank-allagree2 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.0734 - Accuracy: 0.9912 - F1: 0.9911 | 7b997378de436fa2bcb05dcb2f0903a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3209 | 1.0 | 227 | 0.1929 | 0.9558 | 0.9551 | | 0.0821 | 2.0 | 454 | 0.0994 | 0.9867 | 0.9867 | | 0.04 |... | cdebf8ad91a245247bb127275bee2c92 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.05_pretrainedFalse This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 5.2259 | 1f558c7698e76bca5a4e550aa046e432 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.8101 | 1.0 | 1062 | 5.4771 | | 4.3998 | 2.0 | 2124 | 5.2893 | | 4.1418 | 3.0 | 3186 | 5.2259 | | e0bfb2bc0e31474ec50adac9c451c3ff |
agpl-3.0 | ['roberta', 'icelandic', 'norwegian', 'faroese', 'danish', 'swedish', 'masked-lm', 'pytorch'] | false | ScandiBERT Note note: The model has been updated on 2022-09-27 The model was trained on the data shown in the table below. Batch size was 8.8k, the model was trained for 72 epochs on 24 V100 cards for about 2 weeks. | Language | Data | Size | |-----------|------------------------... | ad9102760a740ad4ef60e2ca4327443c |
apache-2.0 | ['classification', 'generated_from_trainer'] | false | clasificador-rotten_tomatoes This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4363 - Accuracy: 0.9138 | 83aeffd257e5a22cccc7d56f2e557165 |
apache-2.0 | ['classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4625 | 1.0 | 853 | 0.3543 | 0.9027 | | 0.2407 | 2.0 | 1706 | 0.3710 | 0.9115 | | 0.0962 | 3.0 | 2559 | 0.4363 | 0.... | 773696418316a762dd02ca0b44c5d344 |
apache-2.0 | ['generated_from_keras_callback'] | false | vishalpc6191/mt5-small-finetuned-amazon-en-es 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: 5.8344 - Validation Loss: 3.7184 - Epoch: 1 | 50108026062fe7ed506027b3c002ca24 |
mit | [] | false | 🇹🇷 BERTurk BERTurk is a community-driven uncased BERT model for Turkish. Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. | 2fc590c2d8452342d28c3b000fc07a46 |
mit | [] | false | Stats The current version of the model is trained on a filtered and sentence segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). Th... | 9a004e9b061da12f5126353a4068380f |
mit | [] | false | Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | --------------------------------- | -------------------------------... | 278bef6acdd29472227050831723a157 |
mit | [] | false | Usage With Transformers >= 2.3 our BERTurk uncased model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased") model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-uncased") ``` | f65687c05747396ba1c2232144e04227 |
mit | ['generated_from_trainer'] | false | kant-gpt2-large This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large). It was trained on the "Akademie Ausgabe" of the works of Immanuel Kant. It achieves the following results on the evaluation set: - Loss: 3.4257 | 46c7ec135ef4cc57893b19f0abccaba7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 | 79213e7fdabe0cfa76676f5c9edd93f3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.4094 | 1.0 | 11308 | 3.3838 | | 3.0445 | 2.0 | 22616 | 3.3107 | | 2.7161 | 3.0 | 33924 | 3.3409 | | 2.4793 | 4.0 | 45232 | 3.4257 ... | 13a24128abdf0b4039fec084a28b48bc |
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