license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: ./conf/tuning/train_tacotron2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/h/tts_train_tacotron2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_wo... | 5702bd6339d2a8ece14da5ec8c8dde56 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Dash Waifu Diffusion Mix This Stable Diffusion model is a merge of a custom model of mine and BasilMix using U-Net Blocks Weight Merge. Inspired by [OrangeMixs](https://huggingface.co/WarriorMama777/OrangeMixs) | 308751fd9583f30398f890a1acef38b0 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Sample Generation  **Prompt:** ``` woman on beach, dutch angle, (partially submerged in shallow water:1.2), detailed bay background, 1girl, sitting on the ground, hands between spread legs, leaning, red bikini, long blo... | 950de149068d91f63d9c326cb76111d4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Recommendations **Drawing art style** - VAE: [orangemix.vae.pt](https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/VAEs/orangemix.vae.pt) - Sampler: DDIM - CFG scale: 3~10 **Realistic art style** - VAE: [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/tree/main) - Samp... | 464152a4810c856ee2bf5df8a2bfdc84 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Generation Comparisons  **Prompt:** ``` realistic polaroid photo by Wong Kar-Wai, cute punk woman, solo, messy grunge hair, outrun red jacket, blue eyes, red lipstick, eye bags, long jeans, smirk, RAW color, high quali... | ba90555fc567fb8bdc360fe70c64976d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_qqp_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4322 - Accuracy: 0.8082 - F1: 0.7405 - Combined Score: 0.7744 | a8f3de1a2b62f8b5d763ea5613e30d8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.5251 | 1.0 | 1422 | 0.5016 | 0.7563 | 0.6686 | 0.7124 | | 0.466 | 2.0 | 2844 | ... | ee59c75a794dcab2bd2c2060376438fc |
mit | ['roberta-base', 'roberta-base-epoch_81'] | false | RoBERTa, Intermediate Checkpoint - Epoch 81 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 7d2c17a3c39ad0bb6db4ea27bfe1e6d1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Turkish Fine-tuned [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760) on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). When using this model, make sure that your speech input is sampled at 16kHz. | 34884e79f8469ed76fe1102e2e201fab |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor... | 2b52a2feb2f2bd61fa40803698ee8caf |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-large-squadshifts-nyt-qg` This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi... | 6a89572b925c97a84ea7974de86b05cb |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.... | 442617a444e0896ec81c846d5c4b6a4f |
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-large-squadshifts-nyt-qg"... | 20337bdb7d66d6dd3bd9905e2d79ea54 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squadshifts-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | 1ec9c76520c429df462996d0e6963263 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-large-squad - max_length: 512 - max_length_output: 32 - epoch: ... | 7647b06f91cfc4d414ebd02c94f6710b |
apache-2.0 | ['generated_from_trainer'] | false | MIX2_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4929 - Otaku Benchmark VN BLEU: 20.21 - Otaku Benchmark LN BLEU: 13.29 - Otaku Benchmark M... | d08993093dc233c9a2eaf9f236c5e91b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.8467 | 0.01 | 2000 | 2.3237 | | 2.6439 | 0.02 | 4000 | 2.2542 | | 2.547 | 0.03 | 6000 | 2.1956 | | 2.4852 | 0.04 | 8000 | 2... | 306cda21c1e740ae0404d04531326af1 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | nl_core_news_lg Dutch pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `nl_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, ... | e16d7604ba639ad86f1239d6a341be2c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (323 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `POS=VERB\|VerbForm=Part`, `POS=PUNCT`, `Number=Sing\|POS=AUX\|Tense... | 4365c19013742c9e4c9bf3e049f5bcbc |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 95.14 | | `SENTS_P` | 85.80 | | `SENTS_R` | 88.88 | | `SENTS_F` | 87.32 | | `DEP_UAS` | 87.02 | | `DEP_LAS` | 82.53 | | `ENTS_P` | 78.51 | | `ENTS_R` | 75.03 | | `ENTS_F` | 76.73 | | `TOKEN_ACC` | 99.94 | | `TOKEN_P` | 99.74 | | `TOKEN_R` | 99.76 | | `TOKEN_F` | 9... | 8272261b4ed27eacdfa39589d7959823 |
mit | ['audio', 'automatic-speech-recognition'] | false | We took `facebook/wav2vec2-large-960h` and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transc... | 921692257d0d2e7ad34ae624fd435c2a |
mit | ['audio', 'automatic-speech-recognition'] | false | load model and tokenizer processor = Wav2Vec2Processor.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency") model = Wav2Vec2ForCTC.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency" filename = "INSERT_FILENAME" audio, sampling_rate = sf.read(filename) input_values = processor... | aa9eaf3769e612d32f0c0d8bff087db5 |
mit | [] | false | Introduction [camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset. Model was trained on wikiner-fr dataset (~170 634 sentences). Model was validated on emails/chat data and overperformed other models on this type of data specifically. In particular the model seems to work better o... | 04a4a4cf5c0062daeb4222fbfa2bdd10 |
mit | [] | false | Load camembert-ner and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner") model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner") | 5b8e416ad4016976b6b799b1f62978fc |
mit | [] | false | Process text sample (from wikipedia) from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wa... | c42faeac67a62334c60ab6b43c7b6cd6 |
mit | [] | false | Model performances (metric: seqeval) Overall precision|recall|f1 -|-|- 0.8859|0.8971|0.8914 By entity entity|precision|recall|f1 -|-|-|- PER|0.9372|0.9598|0.9483 ORG|0.8099|0.8265|0.8181 LOC|0.8905|0.9005|0.8955 MISC|0.8175|0.8117|0.8146 For those who could be interested, here is a short article on how I used... | c46be5982fc5182526b0e59af47fbb91 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3254 - Precision: 0.6102 - Recall: 0.5595 - F1: 0.5838 - Accura... | 649a25bfdd67967b398ed8b47a67b82a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.3324 | 0.3097 | 0.2604 | 0.2830 | 0.8776 | | No log | 2.0 |... | 09c46f8b2f9d306fb99ee7e2cb8d68d0 |
apache-2.0 | ['generated_from_trainer'] | false | tokens 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: 0.9811 - Wer: 0.4608 | 3d39f7056cfa5b29d3ccb3b658d138f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.5212 | 0.59 | 400 | 3.3776 | 1.0 | | 2.4798 | 1.18 | 800 | 1.0697 | 0.7740 | | 1.0057 | 1.77 | 1200 | 0.7077 | 0.648... | 5886b4be5496bae9679359a7c021ac5f |
mit | ['generated_from_trainer'] | false | codeparrot-ds-sample-2ep-29mar 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: - Loss: 1.6283 | 2febbf09487cf19714a97ffc0ea25d95 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sched... | 5793ce5d14d38b17812c157b2f7b7751 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-large_sst2_negation0.5 This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.6661 | 1ef73a87f0dc43d36496678fe61c641e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4111 | 1.0 | 1092 | 3.3396 | | 1.8602 | 2.0 | 2184 | 3.5624 | | 1.5982 | 3.0 | 3276 | 3.6661 | | 291c041ce005f3aec976b9529b7334f8 |
mit | [] | false | swamp-choe-2 on Stable Diffusion This is the `<cat-toy>` 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 ... | a333c53c185039502340c02d559f0708 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. | 02e18e7152c0dc3e62cc3671f96da6b1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-korean-demo-test2 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: 1.0566 - Wer: 0.5224 | 2cc4b93503c68f3622bf4fb5d126fd94 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | c8a25d739b495594b8cb98f788f05cfe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 31.2541 | 0.3 | 400 | 5.4002 | 1.0 | | 4.9419 | 0.59 | 800 | 5.3336 | 1.0 | | 4.8926 | 0.89 | 1200 | 5.0531 | 1.0 ... | f3a3d5dbe01086c482152f2931c71cc0 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v2_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6159 - Precision: 0.08 - Recall: 0.0005 - F1: 0.0010 - Accuracy: ... | eaa5be702ec68bf1441065e29ae0bc6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 16 | 0.7399 | 0.0 | 0.0 | 0.0 | 0.7779 | | No log | 2.0 |... | ed5090e2dacc4184426f0b81e5cd4ce9 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_xls-r_s448 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 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 46b28aac91bade7938f3a880c78f3244 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-dapt-lm-music 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: | 2ca559623883d29e29f9e844f156df4c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | a409c0e5a69f0a26f7fc4e9823214fce |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_gst_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4036264/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 5980b10ce3f7a14d4455e5a21aa49796 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | fa0c0d03ba2f3880f1292bb98cdb1a7a |
apache-2.0 | ['BERT', 'NLU', 'FewCLUE'] | 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 | MegatronBERT | 3.9B | 中文 Chinese | | c23eb490cbd3ebb89147debe314a4ca6 |
apache-2.0 | ['BERT', 'NLU', 'FewCLUE'] | false | 模型信息 Model Information Erlangshen-MegatronBert-3.9B-Chinese是一个比[Erlangshen-MegatronBert-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-MegatronBert-1.3B)拥有更多参数的版本（39亿）。我们遵循原来的预训练方式在悟道数据集（300G版本）上进行预训练。具体地，我们在预训练阶段中使用了封神框架大概花费了64张A100（40G）约30天。 Erlangshen-MegatronBert-3.9B-Chinese (3.9B) is a larger version of [Er... | a0b196704880fa5f03a17a0d6d799913 |
apache-2.0 | ['BERT', 'NLU', 'FewCLUE'] | false | 更多信息 More Information [IDEA研究院中文预训练模型二郎神登顶FewCLUE榜单](https://mp.weixin.qq.com/s/bA_9n_TlBE9P-UzCn7mKoA) 2021年11月10日，Erlangshen-MegatronBERT-1.3B在FewCLUE上取得第一。其中，它在CHIDF(成语填空)和TNEWS(新闻分类)子任务中的表现优于人类表现。此外，它在CHIDF(成语填空), CSLDCP(学科文献分类), OCNLI(自然语言推理)任务中均名列前茅。 On November 10, 2021, Erlangshen-MegatronBert-1.3B topped t... | 8ca8be4de4ca593bb8382f2d405544c7 |
apache-2.0 | ['BERT', 'NLU', 'FewCLUE'] | false | 下游效果 Performance 下游中文任务的得分（没有做任何数据增强）: Scores on downstream Chinese tasks (without any data augmentation): | Model | afqmc | tnews | iflytek | ocnli | cmnli | wsc | csl | | :-------------------------------------... | 007b9292f2d73846a57ace5e62699d86 |
apache-2.0 | ['BERT', 'NLU', 'FewCLUE'] | false | 使用 Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese') text = ... | 94f31af0c0ba555b8d24241da103a284 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in Chinese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyon... | a480ae42edb157567a222b641bb7cc5c |
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... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn") audio_paths... | c2ea6df8304e3311bc9066155c386fae |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | e5ab4a47a1dcbb0c4b4b1c467a38cbb7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Chinese (zh-CN) test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "zh-CN" MODEL_ID = "jonatasgrosman/wav2vec2-large-xls... | f8cfb8e2dac38931fcb353d318312e08 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch... | 428b2e0d61212a488d044965c6d466c3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-chinese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {C}hinese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn}}, y... | fad201025e9438b10b3e8c351f75104f |
apache-2.0 | ['generated_from_trainer'] | false | try-out-model-amc2 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: 0.5462 - F1: 0.8557 | cefa6079d9ec6882fdd4fb97f4437fd3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 7613904504edf631dc474a57fbdcf454 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.8475 | 1.0 | 208 | 2.1236 | 0.4655 | | 1.6756 | 2.0 | 416 | 1.2293 | 0.7030 | | 0.9133 | 3.0 | 624 | 0.8073 | 0.8191 | |... | 0f79aa5c6bcdefbfe93a41c5d4af7947 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | voxforge1-xlsr: Wav2vec 2.0 with VoxForge Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the [VoxForge](http://www.voxforge.org/) dataset. In this notebook the model is tested against other available Brazilian Portuguese datasets. | Dataset |... | cfbb80b3d96c561f97d49045117d8c0e |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | voxforg... | 03f638c8e9d6c9e05735b8bee87921e5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor ... | 3d5075eb448b2fcb72e90ebdd2d5af1d |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.4684840205331983 | 4e0796b820fd2b6547d81e2c641a9cc5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.6080167359840954 | c7bdf1bb6bfd718596ef61bbf5245901 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.5037468434343434 | 2c671ac4ea5f1858a0636e0e33b99a9c |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.505595213971485 | 532fbd644dd2e336769c8e14cc986981 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.7177723323755854 | 44fc54f225b3d7b397a4bc4d7a14fefb |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.7309431974873112 | 71dd0c2a8fdd8f8076de167bc444d5aa |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.5613906926406929 | d823f46706d8463883f2b3775d2810ae |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.32184971297675896 | 00ee2b872f955e1d772dc43446eb741d |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.4707820098981609 | 635e7aae76ab9a7e0941c5e2c5fc7584 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.356227904040404 | 3286211d6d721c05dbd0fb24e2d85321 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.3786376653384398 | 66b384dae1cd88c16f8300cf3c3cbe92 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.5864959640811857 | cb421a906537c595ce6c165c0676dbc5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.6368727228726417 | 51b45472a15f6f372b7e5298c7b9ebb1 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.4279924242424241 | b60398727973b25a5d29bd4cae4bd355 |
mit | [] | false | OnePunchMan on Stable Diffusion This is the `<OnePunch>` 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 ... | 3554715c0236c096fea7ae152fbc342b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-ces_slk-uk Neural machine translation model for translating from Czech and Slovak (cs+sk) to Ukrainian (uk). 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 ... | ebab9c2b95bd693a7701c6a6ea744b97 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-08 * source language(s): ces * target language(s): ukr * model: transformer-align * data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pbt_transformer-align_2022-03-08.zi... | e2fd5e1d839dccc769601a1993538f6f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Replace this with text in an accepted source language.", "This is the second sentence." ] model_name = "pytorch-models/opus-mt-tc-base-ces_slk-uk" tokenizer = MarianTokenizer.from_pretrained(model_na... | ed1078c6d4a5b6c22034c49728af6949 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces+slk-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt) * test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.f... | 2ea46045a4e80dd675c3204cc12ca489 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | ces-ukr | tatoeba-test-v2021-08-07 | 0.66867 | 48.6 | 1787 | 8891 | | ces-ukr | flores101-devtest | 0.51387 | 21.8 | 1012 | 22810 | | slk-ukr | flores101-devtest | 0.51418 | 21.4 | 1012 | 22810 | | 283f94f34ef6d4e20546800865a48a7a |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | roberta-base + DAPT + Domain-Specific QA Objective: This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain. https://huggingface.co/thatdramebaazguy/movie-roberta-base was used a... | 2cb13fc3eb5200b1b806066eea62b3be |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** QA **Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, SQuADv1 **Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) **Infrastructure**: 1x Tesla v100 **Co... | 63db1416956fdcb25d13d2715c606fbd |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-0_austria-10_s381 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | 21c3b9fd57987e041d72b6ed0a2d820b |
apache-2.0 | [] | false | distilbert-base-en-fr-de-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original ac... | 5180a5310682d93febb959a3fb8f2e35 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-de-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-de-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | ee9eb1fda38fc29e31fda8cc6290416b |
apache-2.0 | [] | false | Model description **CAMeLBERT-MSA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the ARZTB dataset . Our fine-tuning procedure and the hyperparameters we used ... | 9ad583af459cccf9566ae86eca89493d |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy') >>> text = 'عامل ايه ؟' >>> pos(text) [{'entity': 'adj', 'score': 0.99979395, 'index': 1, 'word': 'عامل', 'start... | 4d125005445a1b9a5689def4b3922db1 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-13-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 | 5cedf7ac07ccebe7a6456b535679b899 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 - Precision: 0.9274 - Recall: 0.9363 - F1: 0.9319 - Accuracy: 0.9840 | 1b2a87da78afc8aebd7c6ff08fe1d45b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2403 | 1.0 | 878 | 0.0701 | 0.9101 | 0.9202 | 0.9151 | 0.9805 | | 0.0508 | 2.0 |... | b222515479b4c41362c6f4f21895dda2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7702 - Accuracy: 0.9184 | 115198d4f94990468d28dca20a0f35b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2984 | 1.0 | 318 | 3.2941 | 0.7490 | | 2.6352 | 2.0 | 636 | 1.8755 | 0.8410 | | 1.5468 | 3.0 | 954 | 1.1587 | 0.... | 750ea5e7c0a54202cf913a8155544277 |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_1e-05-ep_10-seq_128_bs-16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8391 - Bleu: 0.0308 - Meteor: 0.1222 | 7dd20cd7a900256402af406ad2aa55ee |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 30bc69e153182b2bca31127fb81183f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 404 | 3.1172 | 0.0194 | 0.0879 | | 3.6071 | 2.0 | 808 | 2.9990 | 0.0251 | 0.1066 | | 3.2935 | 3.0 |... | 4605e049d34dfdbf636099545ed78b68 |
mit | ['question-generation'] | false | T5 for question-generation This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. You can play with the model using the inference API, just highlight the answer spans with `<hl>` tok... | 4274b17cd0b713ed7d4ea0eea8f13a3a |
mit | ['question-generation'] | false | Model in action 🚀 You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) ```python3 from pipelin... | 82e9c4aebc3a1e73b7fbed87fd1a0582 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.