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 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/all-with_prefix-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
prefix = "answer2question"
text = "Python is an interpreted,... | 84ed2a70b77c856e2e8551b1892387d1 |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 575k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces.
This model was train... | f47a71fb4ce8f6fc9a0b6b2b4ede7298 |
apache-2.0 | [] | false | Prefix
This model was trained **with a prefix**: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different.
E.g. the above text about Python produces the following output:
| Prefix | Output |
| --- | --- |
| an... | d904895c898b2209d91cd5c8393b474c |
mit | ['generated_from_trainer'] | false | roberta-base-sst2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Accuracy: 0.9323 | 665c1293666a4ffbbdbbb8ac6c9bbc66 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.575 | 0.12 | 500 | 0.2665 | 0.9071 | | 0.2989 | 0.24 | 1000 | 0.2088 | 0.9220 | | 0.2725 | 0.36 | 1500 | 0.2560 ... | 2672dfe572bed1263d50a975ad6a6588 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper largeV2 German MLS This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech german dataset. It achieves the following results on the evaluation set: - Loss: 0.1370 - Wer: 6.0483 | 33701223b9454db2988205cdb595b7df |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The model is fine-tuned for 4000 updates/steps on multilingual librispeech German train data. - Zero-shot - 5.5 (MLS German test) - Fine-tune MLS German train - 6.04 (MLS German test) Even after fine-tuning the model is doing slightly worse than the zero-shot. | 0be80a990791aceabc9ebe6f9879dfc4 |
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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 5fd1e36165c7018051560bca7eacd146 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1755 | 0.25 | 1000 | 0.1844 | 7.7118 | | 0.1185 | 0.5 | 2000 | 0.1636 | 7.0659 | | 0.1081 | 0.75 | 3000 | 0.1396 | 6.0844 | |... | 463c7ad3beec77fa98e2e4ea58ea348a |
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: 0.1371 - F1: 0.8604 | a9825ec9149558a9f281cd572a3483f9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2584 | 1.0 | 525 | 0.1675 | 0.8188 | | 0.127 | 2.0 | 1050 | 0.1383 | 0.8519 | | 0.0781 | 3.0 | 1575 | 0.1371 | 0.8604 | ... | 50f5f4408d1e9f370ba38f793faa168f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 167 | 2.2978 | 31.8313 | 10.3824 | 29.6182 | 29.4336 | 10... | 61552e31559179e56fd1b44c978da22d |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | GTA5 Artwork Diffusion: Source(s): [Hugging Face](https://huggingface.co/ItsJayQz/GTA5_Artwork_Diffusion) - [CivitAI](https://civitai.com/models/1309/gta5-artwork-diffusion) GTA5 Artwork Diffusion This model was trained on the loading screens, gta storymode, and gta online DLCs artworks. Which includes characters, ba... | 56bc7151e8656278227ee7548ab92620 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-zle-fi Neural machine translation model for translating from East Slavic languages (zle) to Finnish (fi). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the ... | 7fabea34e6ea8e246baf91bec25d2970 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-07 * source language(s): rus ukr * target language(s): fin * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-07.zip]... | 7f034038bbf39a77de5725c7ce71e321 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Мы уже проголосовали.", "Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять." ] model_name = "pytorch-models/opus-mt-tc-big-zle-fi" tokenizer = MarianTokenizer.from_pretrained(model_na... | 602643e5683cb4f9447b20e55f49b54f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-fi") print(pipe("Мы уже проголосов... | d363d8af9e5630362162d5f8013a3de1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-fin/opusTCv20210807+bt_transformer-big_2022-03-07.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 5ba4e5c9d60d477e58a2a8e402aaf039 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | rus-fin | tatoeba-test-v2021-08-07 | 0.66334 | 42.2 | 3643 | 19319 | | rus-fin | flores101-devtest | 0.52577 | 17.4 | 1012 | 18781 | | ukr-fin | flores101-devtest | 0.53440 | 18.0 | 1012 | 18781 | | d36c020ef0b824b9c19bf3c2ae5501a0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.7713 | dcb0a94253d485f44f1ef4a30f3eef28 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0325 | 1.0 | 585 | 1.7520 | | 1.609 | 2.0 | 1170 | 1.7713 | | aeb2cc4aa40ac23e11739c35bab1de2e |
apache-2.0 | ['generated_from_trainer'] | false | hubert-base-ls960-finetuned-speech-commands This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9964 - Loss: 0.0192 | d32f6ba6861fe81224bbca46895fde2f |
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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | a15f1c3d5b1ecd19ccc6d6b2186d728e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.1264 | 1.0 | 8558 | 0.9950 | 0.0242 | | 0.0637 | 2.0 | 17116 | 0.9964 | 0.0165 | | 0.0694 | 3.0 | 25674 | 0.9961 | 0.02... | bf0ce73011a062f0215f09c5afb1d395 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_trai... | 3a1080de6349676b574c3dfc451df2cb |
apache-2.0 | ['korean', 'klue', 'summarization'] | false | Overview Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language model to address the aforementioned shortcoming... | 9164815cf96f2e72a92afbbf74870151 |
apache-2.0 | ['korean', 'klue', 'summarization'] | false | Object Self-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]] were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project. | 4959d19d4c7278dbba2b111af1ce8608 |
apache-2.0 | ['korean', 'klue', 'summarization'] | false | Config - **KoMiniLM-23M** ```json { "architectures": [ "BartForPreTraining" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 384, "initializer_range": 0.02, "intermediate_size": 1536, "layer_norm_eps": 1e-12, ... | 1a88716adeffa6ac6f5ebffc37e05948 |
apache-2.0 | ['korean', 'klue', 'summarization'] | false | Param | Average | NSMC<br>(Acc) | Naver NER<br>(F1) | PAWS<br>(Acc) | KorNLI<br>(Acc) | KorSTS<br>(Spearman) | Question Pair<br>(Acc) | KorQuaD<br>(Dev)<br>(EM/F1) | |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:| |KoBERT(KLUE)| 110M | 86.84 | 90.20±0.07 | 87.11±0.05 | 81.36±0.21 | 81.06±0.33 |... | 039fb200225b364e8172ede618f353f4 |
apache-2.0 | ['korean', 'klue', 'summarization'] | false | Reference - [KLUE BERT](https://github.com/KLUE-benchmark/KLUE) - [KcBERT](https://github.com/Beomi/KcBERT) - [SKT KoBERT](https://github.com/SKTBrain/KoBERT) - [DistilKoBERT](https://github.com/monologg/DistilKoBERT) - [lassl](https://github.com/lassl/lassl) | 255aeeb3ba5c09d6a71bee0d4fdda5fe |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | wav2vec 2.0 with CTC/Attention trained on DVoice Kabyle (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [CommonVoice](https://commonvoice.mozilla.org/) Kabyle dataset within SpeechBrain. For a better experience, we encourage you... | 5c7fc86a8f5333d09657eff806482d9a |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Kabyle) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-kabyle", savedir="pretrained_models/asr-wav2vec2-dvoice-wol") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` | 5bc364466ed605fbf1c805dd656f05f2 |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.2 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0187 - Precision: 0.9160 - Recall: 0.8752 - F1: 0... | 02c0acd795ebeeebe89eeff1f39521f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 245 | 0.0250 | 0.8990 | 0.8225 | 0.8591 | 0.9919 | | No log | 2.0 |... | 757f3952e2293d1a16a1fbf1ba1a60f7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-checkpoint-7.1 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-6](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-6) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9369 - Wer: 0.3243 | 4315165deccad69fd548588514ca2702 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.3124 | 1.75 | 1000 | 0.5602 | 0.3403 | | 0.2428 | 3.5 | 2000 | 0.5924 | 0.3431 | | 0.1884 | 5.24 | 3000 | 0.6161 | 0.342... | cb94bdd407e4d6a36daff0db337ab444 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en` This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | 6022917df7b90b1ef853969b7af26381 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sat Jul 3 23:10:19 JST 2021` - python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.0` - Git hash: `0f7558a716ab830d0c29da8785840124f358d47b` - Commit date: `Tue Jun 8 15:33:49 2021 -0400` | 2f9cd0486b4c5e8be40c4488e2c88886 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.3|1.6|0.2|0.2|1.9|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|... | ee63173ca037980f258762e2e6df8513 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.5|0.2|0.2|0.2|0.6|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864... | 80074b754cacda07506758d39845591e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.6|24.9| |decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|... | a6c021d51ed80566b34582857d67264a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Training config See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml) ```yaml config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conform... | 5d3182a4df8d43a2e3aa260e1f6b5f02 |
apache-2.0 | ['summarization', 'bart'] | false | BART base model fine-tuned on CNN Dailymail - This model is a [bart-base model](https://huggingface.co/facebook/bart-base) fine-tuned on the [CNN/Dailymail summarization dataset](https://huggingface.co/datasets/cnn_dailymail) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp). The Bart model was proposed ... | 312d9527a5b00a3a8cc15c3e31e7c7a0 |
apache-2.0 | ['summarization', 'bart'] | false | Encode Input Text input_text = '(CNN) -- South Korea launched an investigation Tuesday into reports of toxic chemicals being dumped at a former U.S. military base, the Defense Ministry said. The tests follow allegations of American soldiers burying chemicals on Korean soil. The first tests are being carried out by a j... | 05c960a9ae0d6ca92e80b2881df75936 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | bootstrap | True | | ccp_alpha | 0.0 | | class_weight | | | criteri... | ec3cc74898fc63291f19f730d021ec24 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>RandomForestClassifier(n_estimators=25, n_jobs=-1, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the... | 57cf838d680a5f501b1e4df748c31621 |
mit | ['deberta-v1', 'deberta-mnli'] | false | DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repositor... | 4ce697bc97ecada21981b46cd631c4b0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_qnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6656 - Accuracy: 0.5905 | 94e322ae723a47bf70bba4de7a6135d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 1.0 | 410 | 0.6893 | 0.5654 | | 0.6702 | 2.0 | 820 | 0.6656 | 0.5905 | | 0.6477 | 3.0 | 1230 | 0.6665 | 0.... | 670ef5896bb0bb1815673082a026df0b |
mit | [] | false | Alberto_Montt on Stable Diffusion This is the `<AlbertoMontt>` 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... | b8c04c7a933d65abfd62dab7989dc7e5 |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2861 - Validation Loss: 0.4212 - Epoch: 1 | dcc765c4bf9ea549fdc717c456b8afe7 |
mit | [] | false | Harley Quinn on Stable Diffusion This is the `<harley-quinn>` 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 ... | cdf45dd0f5041024df527d91ebbfcf67 |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a PhoBERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [phobert-base](https://huggingface.co/vinai/phobert-base). | de764b4de867f862001c782810c5c32f |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py class UDgoeswithViNLP(object): def __init__(self,bert): from transformers import AutoTokenizer,AutoModelForTokenClassification from ViNLP import word_tokenize self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForTokenClassification.from_pretrained(bert) self... | 881782a75c74760ceaa77425d67b3dec |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" q=[self.model.config.id2label[p[i,j]].split("|") for i,j in enumerate(h)] t=[i.replace("_"," ") for i in t] if len(t)!=len(v)-2: t=[z.pop(0) if i==self.tokenizer.unk_token else i.replace("_"," ") for i in self.tokenizer.convert_ids_to_tokens(v[1:-1])] for i,j in reversed(list... | 6bfa2ae8170d110223d4c17f01bcf1d2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hu', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-hungarian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HU dataset. It achieves the following results on the evaluation set: - Loss: 0.2562 - Wer: 0.3112 | 4b57ef206771b69dfb6debf60c9ca287 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hu', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50.0 - mixed_precision... | 16a7bd3412f130c82effb9db939dc4c6 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hu', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.3964 | 3.52 | 1000 | 1.2251 | 0.8781 | | 1.3176 | 7.04 | 2000 | 0.3872 | 0.4462 | | 1.1999 | 10.56 | 3000 | 0.3244 | 0.392... | 4e4d1b70b6a7c45e13799c38aab80e92 |
[] | ['non-nn', 'language', 'text', 'text-generation'] | false | 39;s keyboard word suggestions. Also this model should be capable to train for a conversation but the tests on a conversational dataset say that it's very bad at conversations. Also I still don't understand how to make the widget work, it appeared but how does it "talk" with the model? So here's the space for this mode... | 7ceab04472499116a69c471df6910ada |
[] | ['non-nn', 'language', 'text', 'text-generation'] | false | Model Card Authors <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> Ierhon | 372d783c55d81b7f4047fc6429f6f83c |
mit | [] | false | kysa-v-style on Stable Diffusion This is the `<kysa-v-style>` 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 ... | fb783517ecc33d8b9b5ddb3a3dbc6797 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for levit_conv_256.fb_dist_in1k A LeViT image classification model using default linear mode (non-convolutional mode with nn.Linear and nn.BatchNorm1d). Pretrained on ImageNet-1k using distillation by paper authors. | 3e6d39ec9cc2d4c8b14140ba0d5a4369 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('levit_conv_256.fb_dist_in1k', pretrained=True) model =... | b97dc44e15da1cf8212088fd72e36b40 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_conv_256.fb_dist_in1k', pretrained=True, ... | 432dbbf00218af816f8bb773d45e2e92 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_conv_256.fb_dist_in1k', pretrained=Tr... | de046c8f8fe4029b4ab21b5e9d36e1f9 |
apache-2.0 | ['generated_from_trainer'] | false | MilladRN 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: 3.4355 - Wer: 0.4907 - Cer: 0.2802 | 8f2cd7c5796eea6c4b489d178d509966 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - 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: 4000 - num_epochs: 750 - mixed_precision_t... | 724a01cd4dce43de36c194ba1439f14c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | 3.3347 | 33.9 | 2000 | 2.2561 | 0.9888 | 0.6087 | | 1.3337 | 67.8 | 4000 | 1.8137 | 0.6877 | 0.3407 | | 0.6504 |... | 542fedfc0d800a1b2f1e03ca8fbfd79c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 66ca5df9f08b6084dbde4d9f312fa8ba0a47ecfc pip install -e . cd egs2/americasnlp22/asr1 ./run.sh \ --skip_data_prep false \ --skip_train true \ --download_model espnet/americasnlp22-asr-gvc \ --lang gvc \ --local_data_opts "--lang gvc" \ ... | aabd4ce459497ed82254aebbc8f414ae |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Jun 5 03:29:33 CEST 2022` - python version: `3.9.13 (main, May 18 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-2)]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0+cu115` - Git hash: `d55704daa36d3dd2ca24ae3162ac40d81957208c` - Commit date: `Wed Jun 1 02:33:09... | cac9522e9e434dfc242691f2987dca99 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_raw_gvc_bpe100_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// d... | 69018b930f5ff34e64ecc99873ee02b0 |
mit | ['exbert'] | false | CXR-BERT-specialized [CXR-BERT](https://arxiv.org/abs/2204.09817) is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization, and text augmentations. The resulting model demonstrates improved performance on radiology natural langu... | 185ae3c8df0e4a624fcacee478399c93 |
mit | ['exbert'] | false | Model variations | Model | Model identifier on HuggingFace | Vocabulary | Note | | ------------------------------------------------- | ------... | e56988f9b79acf57cd0c96d054ae30d0 |
mit | ['exbert'] | false | Image model **CXR-BERT-specialized** is jointly trained with a ResNet-50 image model in a multi-modal contrastive learning framework. Prior to multi-modal learning, the image model is pre-trained on the same set of images in MIMIC-CXR using [SimCLR](https://arxiv.org/abs/2002.05709). The corresponding model definitio... | 09d097514434258d5bfda4678e9f12d5 |
mit | ['exbert'] | false | Citation The corresponding manuscript is accepted to be presented at the [**European Conference on Computer Vision (ECCV) 2022**](https://eccv2022.ecva.net/) ```bibtex @misc{https://doi.org/10.48550/arxiv.2204.09817, doi = {10.48550/ARXIV.2204.09817}, url = {https://arxiv.org/abs/2204.09817}, author = {Boeckin... | bfe64b7beb887d086b75611a8787e908 |
mit | ['exbert'] | false | Primary Intended Use The primary intended use is to support AI researchers building on top of this work. CXR-BERT and its associated models should be helpful for exploring various clinical NLP & VLP research questions, especially in the radiology domain. | 44ecb4d1c77fd4862dfb90117b375460 |
mit | ['exbert'] | false | Out-of-Scope Use **Any** deployed use case of the model --- commercial or otherwise --- is currently out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are not intended for deployed use cases. Please refer to [the associated paper](ht... | 94c985f2ca86eb40f8d94c609ad2157b |
mit | ['exbert'] | false | How to use Here is how to use this model to extract radiological sentence embeddings and obtain their cosine similarity in the joint space (image and text): ```python import torch from transformers import AutoModel, AutoTokenizer | e5b00ff95ecca16c5de7e7e471ad24b7 |
mit | ['exbert'] | false | Load the model and tokenizer url = "microsoft/BiomedVLP-CXR-BERT-specialized" tokenizer = AutoTokenizer.from_pretrained(url, trust_remote_code=True) model = AutoModel.from_pretrained(url, trust_remote_code=True) | bfec48f8aedf66f85f87ad30bbc75e89 |
mit | ['exbert'] | false | Input text prompts (e.g., reference, synonym, contradiction) text_prompts = ["There is no pneumothorax or pleural effusion", "No pleural effusion or pneumothorax is seen", "The extent of the pleural effusion is constant."] | 542078cfaeaec865bee517ffa8b0fd27 |
mit | ['exbert'] | false | Tokenize and compute the sentence embeddings tokenizer_output = tokenizer.batch_encode_plus(batch_text_or_text_pairs=text_prompts, add_special_tokens=True, padding='longest', ret... | a58fc0a315c7b642bf5cec3719bf63e3 |
mit | ['exbert'] | false | Data This model builds upon existing publicly-available datasets: - [PubMed](https://pubmed.ncbi.nlm.nih.gov/) - [MIMIC-III](https://physionet.org/content/mimiciii/) - [MIMIC-CXR](https://physionet.org/content/mimic-cxr/) These datasets reflect a broad variety of sources ranging from biomedical abstracts to intensi... | c41b72a44981941b80a1f4cf301b32f6 |
mit | ['exbert'] | false | Performance We demonstrate that this language model achieves state-of-the-art results in radiology natural language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports. A highlight of comparison to other common mod... | ba802bffc395ee22ba5c20cdc258c213 |
mit | ['exbert'] | false | tokens after tokenization | Vocabulary size | | ----------------------------------------------- | :-------------------------------: | :----------------------: | :------------------------------: | :-------------: | | RadNLI baseline | 53.30 | - ... | d05e40a3b47174401c1a53202bd25da7 |
mit | ['exbert'] | false | Further information Please refer to the corresponding paper, ["Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing", ECCV'22](https://arxiv.org/abs/2204.09817) for additional details on the model training and evaluation. For additional inference pipelines with CXR-BERT, please refer to... | 3aeae38277905966fda98af399bad30e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5869 - Wer: 0.7266 | d64f567143ac260b846752a980cc535f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2913 | 3.39 | 400 | 1.0961 | 0.9474 | | 0.5857 | 6.78 | 800 | 0.5869 | 0.7266 | | f43c9cdc0d3a7ad1753b34580c212e76 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Thai Combined Concat This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 th dataset and additional scraped data. It achieves the following results on the evaluation set: - Loss: 0.5034 - Wer: 27.2794 (witho... | 9c44a4e2a2b854f2deaf32d51380cc0c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 83.33 | 5000 | 0.5034 | 27.2794 | | d1570ab13670745df8032413b8a2bccf |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_full_band_vits_prosody` ♻️ Imported from https://zenodo.org/record/5521340/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 217f0aab0bbf7e4fa45d8b3a7a49e42f |
mit | ['generated_from_trainer'] | false | outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1299 - F1: 0.7010 | 80c5e74d9da93509ef09c5b8a9a1e04e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 | 88468346f37b17da47844d06b3c6181a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 23 | 0.2190 | 0.7611 | | No log | 2.0 | 46 | 0.1212 | 0.2309 | | No log | 3.0 | 69 | 0.1235 | 0.6229 | |... | 90d02adcbd7428685f25dd179bba8f8e |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s646 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_... | 43f754907c94242b9f784370c807d585 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-10_nortepeninsular-0_s61 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this ... | 4154a4e0312399a5b15b48bbb3189e1d |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-imdb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5591 - Accuracy: 0.866 | 73e0373208f62300f882956bd75a1ee0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.4995 | 0.79 | | No log | 2.0 | 250 | 0.4000 | 0.854 | | No log | 3.0 | 375 | 0.5591 | 0.... | 6198994b96624c76b708193c342287be |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v8_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1980 - Precision: 0.6780 - Recall: 0.7117 - F1: 0.6945 - Accuracy: ... | a0c1e20f3e5eabfd56b838d9113627a0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 64 | 0.2758 | 0.5405 | 0.5298 | 0.5351 | 0.9135 | | No log | 2.0 |... | ed21e475b0ddde81c6fb30a603612039 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-target-glue-rte This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc](https://huggingface.co/muhtasham/small-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5597 - Accuracy: 0.6245 | 6f0bcdf210f545a5026bd8d00abf409f |
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