license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
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mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | Accent Identification from Speech Recordings with ECAPA embeddings on CommonAccent This repository provides all the necessary tools to perform accent identification from speech recordings with SpeechBrain. The system uses a model pretrained on the CommonAccent dataset in English (16 accents). The provided system can ... | c1e10954798bd1aa7110ad521ebfb95c |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | To UPDATE ALL BELOW For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is: | Release | Accuracy (%) |:-------------:|:--------------:| | 30-06-21 | 85.0 | | be07bcf22f3f33a0cad72e077efc9a89 |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | Pipeline description This system is composed of an ECAPA model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resamp... | e3190095bf060239ee8be5e543ea52b4 |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | Perform Language Identification from Speech Recordings ```python import torchaudio from speechbrain.pretrained import EncoderClassifier classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa") | d9b1e1f61eda4dd4ee9808976a2e8f9e |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | Training The model was trained with SpeechBrain (a02f860e). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/Common... | 98b8c7c9da0060ea0ea2fe2abeb50bb9 |
mit | ['audio-classification', 'speechbrain', 'embeddings', 'Accent', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'CommonAccent'] | false | Referencing ECAPA ```@inproceedings{DBLP:conf/interspeech/DesplanquesTD20, author = {Brecht Desplanques and Jenthe Thienpondt and Kris Demuynck}, editor = {Helen Meng and Bo Xu and Thomas Fang Zheng}, title = {{ECAPA-TDNN:} Emphasized Channel ... | f85df468bc08106af1b3c37f53f5930d |
apache-2.0 | ['generated_from_trainer'] | false | tuto-distilbert-base-uncased-mlm This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6807 | afd3190cd665536c64689e9e6d1b890d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8087 | 1.0 | 157 | 0.7144 | | 0.7182 | 2.0 | 314 | 0.6918 | | 0.7041 | 3.0 | 471 | 0.6918 | | 4de3f0350a297fbcaa04e9a0bc262ae9 |
apache-2.0 | ['speech'] | false | Data2Vec-Audio-Base [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: Thi... | 0c20841afc004b6fbb9f775ab959f6a7 |
mit | ['generated_from_keras_callback'] | false | vanichandna/xlmroberta-squad This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an SQuAD v1.1 dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6636 - Epoch: 2 | 869bd808db466a26ca1be5ec5373c06f |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16476, 'end_learning_r... | cf0e7455f5de47792ca5b03b4e83a655 |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Post-training static quantization This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224). The calibration dataload... | 4e11a50a37141b42a562b494930654fd |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Load with Intel® Neural Compressor: ```python from neural_compressor.utils.load_huggingface import OptimizedModel int8_model = OptimizedModel.from_pretrained( 'Intel/vit-base-patch16-224-int8-static', ) ``` | 74ad1afef1b25af03261474bd6194595 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Samples The top 2 images are "pure", the rest could be mixed with other artists or modifiers. I hope it still gives you an idea of what kind of styles can be created with this model. <img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/index.png" width="256px"/> <img src="https://huggingface.co/Froddan/n... | cde23f4251ef4e12aea6af2fa098ef65 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_analysis_model_amzn_imdb_kag_ylp 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: 0.1387 - Accuracy: 0.9527 - F1: 0.9524 | 14438bd9607529c802c14fd0a0c62730 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_hubert_s358 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech in... | a1d075f3ffdd5e49082e2b8212ea5f0f |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-sv_s93 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | f5e3d5e56bc2c34f25207d267e96763e |
cc-by-4.0 | [] | false | FiD model trained on TQA -- This is the model checkpoint of FiD [2], based on the T5 large (with 770M parameters) and trained on the TriviaQA dataset [1]. -- Hyperparameters: 8 x 40GB A100 GPUs; batch size 8; AdamW; LR 3e-5; 30000 steps References: [1] TriviaQA: A Large Scale Dataset for Reading Comprehension an... | 0ec2d29cc4702e12513c475293ffe8cc |
cc-by-4.0 | [] | false | Model performance We evaluate it on the TriviaQA dataset, the EM score is 68.5 (0.8 higher than the original performance reported in the paper). <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> --- license: cc-by-4.... | d04dbabe0a82b67cf1b4c9b24a53b0fe |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_pn_summary_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pn_summary dataset. It achieves the following results on the evaluation set: - Loss: 0.1125 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 19.0 | 1b7fadc98b5f4724f00dabbf5c82de9d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 2 - mixed_precision_training: Native AMP | c4a5f192afbcb4aa673f8d31ca031527 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.1169 | 1.0 | 5127 | 0.1130 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 ... | 3a3969dbc1068bc2f2f4ef910a8a3341 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-imdb-target-conll2003 This model is a fine-tuned version of [muhtasham/small-mlm-wikitext](https://huggingface.co/muhtasham/small-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1138 - Precision: 0.8869 - Recall: 0.9189 - F1: 0.9026 - Accuracy: 0.9777 | 1d809323e382304f7883f80413888c30 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Dutch Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 444fddede69c16a8a567480c04ed03af |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "nl", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 6cc71adb6da226357756c3bfe15cca98 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Dutch test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "nl", split="test") wer =... | 4429388191f872fec621e1b50f27f4ad |
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"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits,... | ab8922110eade51887e6c521ce7232b2 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-dialogsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2771 - Rouge1: 36.5788 - Rouge2: 13.75 - Rougel: 30.9066 - Rougelsum: 32.8118 - Gen Len: 18.846 | 2b617ca29237c3d154e4055aad03affe |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 40f069f9275c0bc4fd9765563c3ae47a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.4705 | 1.0 | 4154 | 1.3514 | 34.3952 | 11.8123 | 28.9797 | 31.003 |... | 19dffb4801e4fbaa7cb19d473f6a92a8 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_final_09 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0816 - Train Accuracy: 0.0343 - Validation Loss: 0.5877 - Validation Accuracy: 0.0313 - Epoch: 24 | c24e7e6da5f6abc8167e5969ed8af4f8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0832 | 0.0116 | 4.4298 | 0.0124 | 0 | | 4.3130 | 0.0131 | 4.0733 | 0.0141 ... | a817a7566517806f5800786ee2f3e021 |
creativeml-openrail-m | ['stable-diffusion'] | false | **Couch Diffusion**  [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/couch-diffusion/resolve/main/couch-diffusion-V1.ckpt) - This is a dreambooth trained on... couches In your prompt, use the activation token: `couch` ... | 0358f661cd9fa61d3e08259438342f01 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2740 - F1: 0.7919 | 5da34e2d4f273cc2abee250a7730512f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8185 | 1.0 | 70 | 0.3369 | 0.7449 | | 0.2899 | 2.0 | 140 | 0.2740 | 0.7919 | | a3a4ffa980c9141dc3f6849c6dc801fc |
mit | ['generated_from_trainer'] | false | bart-large-cnn-samsum-ElectrifAi_v9 This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2325 - Rouge1: 55.1928 - Rouge2: 33.3871 - Rougel: 43.865 - Ro... | 5a269ebf6538c70ff7ddbd6cda6565d6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 27 | 1.2252 | 55.969 | 34.0884 | 43.1389 | 54.7972 | ... | ed1108ba0bc7ff7755fd99afb834df30 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s1 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_7_... | fbc22c5be4567577e1ac21016eda5a34 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.0721 | 82c6c0b534b4f15c025b8341e25e4a3e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | aa1cd65e0a4fc1ad76b214ac2d50ff14 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 391 | 7.2240 | | 7.6715 | 2.0 | 782 | 7.0516 | | 7.0737 | 3.0 | 1173 | 7.0823 | | ed1c8b24bc1175b9f43d9d727707a049 |
other | [] | false | Air Duct Cleaning Richardson TX https://carpetcleaning-richardson.com/air-duct-cleaning.html (972) 454-9815 Do you require a cleaning service from professionals with years of experience?If so, contact us right away.We have been working to improve customers' homes' climates for a long time and can also assist you.Becau... | 1e97f4a285bd4f87545764d51942e3f4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1649 - Accuracy: 0.9325 - F1: 0.9327 | 393908409e0b823b52ed003f35668b04 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.2838 | 0.9065 | 0.9036 | | No log | 2.0 | 500 | 0.1795 | 0.9255 | 0.9255 | | No log |... | c706895b70228c3f6cb728b0a35bef42 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-23 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: 3.1230 - Wer: 1.0 | fc08b98d82ed5f17ea87664c1819ba19 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | 1ce94387b55508dee02edbbb27ad7e19 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.2642 | 1.37 | 200 | 2.9756 | 1.0 | | 2.8574 | 2.74 | 400 | 3.1631 | 1.0 | | 2.8588 | 4.11 | 600 | 3.1208 | 1.0 | | 2.8613 ... | f016247cb04eb193264bc16a6860c0af |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-ces_slk Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk). 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 t... | da870b258c5f3791bcfafb56419dad80 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): eng * target language(s): ces * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](htt... | 2cfaa446341a1757423e66104d3d3b62 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ces<< We were enemies.", ">>ces<< Do you think Tom knows what's going on?" ] model_name = "pytorch-models/opus-mt-tc-big-en-ces_slk" tokenizer = MarianTokenizer.from_pretrained(model_name) model = ... | 92592ba860cbcfa7ec0d3e7bb1621a83 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Myslíš, že Tom ví, co se děje? ``` 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-en-ces_slk") print(pipe(">>ces<< We were enemies.")) | 4524d885e7fcab2b3b2de6e77e177356 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ces+slk/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba... | fae527e70a640bf2bc71113bf5d7e33c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-ces | tatoeba-test-v2021-08-07 | 0.66128 | 47.5 | 13824 | 91332 | | eng-ces | flores101-devtest | 0.60411 | 34.1 | 1012 | 22101 | | eng-slk | flores101-devtest | 0.62415 | 35.9 | 1012 | 22543 | | eng-ces | multi30k_test_2016_flickr | 0.58547 | 33.4 |... | 29816a03a164aa9d3f072d11b6a1cc03 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2619 - F1: 0.8095 | a51c82c549840ec6daae65117fc35d6f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7908 | 1.0 | 70 | 0.3093 | 0.7437 | | 0.2824 | 2.0 | 140 | 0.2580 | 0.8015 | | 0.1834 | 3.0 | 210 | 0.2619 | 0.8095 | ... | 2bac26faa378930a8fdb97145bcd621b |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Wav2Vec2-base-fi-voxpopuli-v2 for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-base-fi-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fi-voxpopuli-v2) for Finnish ASR. The model has been fine-tuned with 276.7 hours of Finnish transcribed speech data. Wav2Vec2 was introduc... | eceb55a8f5c84498d8d115923371007d |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Model description [Wav2vec2-base-fi-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fi-voxpopuli-v2) is Facebook AI's pretrained model for Finnish speech. It is pretrained on 14.2k hours of unlabeled Finnish speech from [VoxPopuli V2 dataset](https://github.com/facebookresearch/voxpopuli/) with the wav2ve... | d246df5969c51f79b23c5bc9a71de5bc |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. | 6abce4fca9607e85fb886886096aed9b |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very lo... | bc36264e0cc0234ff7fbfd860c990dc3 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training data This model was fine-tuned with 276.7 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:----------------------------------... | f8f7cfdc670eba6a0da32c2eb508448a |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training procedure This model was trained on a Tesla V100 GPU, sponsored by Hugging Face & OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only... | 59ba7f61d5a8caf4e3366bda4f6458b9 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-04 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedul... | 1e46257a50c5ac72455b7a374ca3205f |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.575 | 0.33 | 500 | 0.7454 | 0.7048 | | 0.5838 | 0.66 | 1000 | 0.2377 | 0.2608 | | 0.5692 | 1.0 | 1500 | 0.2014 | 0.224... | c9a6448e34d82015bc024e35f9cf2947 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0), [Common Voice 9.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) and with the [FLEURS ASR Finnish test split](https://hu... | 58119e900c673c2968099c3282ccb61f |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Common Voice 7.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the first row of the table) achieves the followi... | 71cc4001f317fa7caadb6da0faeb5f74 |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Common Voice 9.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned --dataset mozilla-foundation/common_voice_9_0 --config fi --split test ``` This model (the first row of the table) achieves the followi... | 93490cecddc41b6d1a775d09b26421ad |
apache-2.0 | ['automatic-speech-recognition', 'fi', 'finnish', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | FLEURS ASR testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned --dataset google/fleurs --config fi_fi --split test ``` This model (the first row of the table) achieves the following WER (Word Error Rate) ... | 5bce2d4a7c2e1cd5d483b54a9fe634e4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0596 - Precision: 0.9397 - Recall: 0.9546 - F1: 0.9471 - Accuracy: 0.9873 | 88bf80d2dce8a29a6172e208926c1bf8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0787 | 1.0 | 1756 | 0.0604 | 0.9250 | 0.9418 | 0.9333 | 0.9844 | | 0.0318 | 2.0 |... | 839f2a201aec0d86f63a0f0890f057fa |
apache-2.0 | ['generated_from_trainer'] | false | vit-airplanes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0152 - Accuracy: 1.0 | a1e85bdf245c608a6e92ceb7085f4a6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0165 | 2.38 | 100 | 0.0152 | 1.0 | | ca2c4ed270de4defc6bc3850c1437f57 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | CRDNN with CTC/Attention trained on Switchboard (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on Switchboard (EN) within SpeechBrain. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.... | f195e5fc24c6d5a172178de78fa67f5a |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed with 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units trained on the training transcriptions of the Switchboard and Fisher corpus. - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convo... | b0457401e3b7b5bcbcdbd85c3b6c8b9e |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Note that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | a92fc67a282aecc36e42626a84fc0eb5 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Transcribing Your Own Audio Files ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-switchboard", savedir="pretrained_models/speechbrain/asr-crdnn-switchboard") asr_model.transcribe_file('speechbrain/asr-crdnn-switchboard/example.w... | 6f21e953eed6ec0b65a00fafb29229a8 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (commit hash: `70904d0`). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ... | 483db863bf5ce98694303986fe699501 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. - Website: https://speechbrain.github.io/ - GitHub: https://github.com/speechbrain/speechbrai... | 1127bdf051a888c6398d9960c320b0fd |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Citing SpeechBrain Please cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and N... | bd08fad6520823196f5eb8e587d3ef5d |
apache-2.0 | ['translation'] | false | opus-mt-en-mr * source languages: en * target languages: mr * OPUS readme: [en-mr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-mr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | bf27470b140a4b6be60f16694479cf31 |
apache-2.0 | ['translation'] | false | opus-mt-da-de * source languages: da * target languages: de * OPUS readme: [da-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://... | e03ceccf46a361039cd56893db22cf03 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.3502 - Pearson: 0.1595 - Spearmanr: 0.1783 - Combine... | a6da4eec8785e1305592c67224c7cd09 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 0.535 | 1.0 | 2518 | 1.3502 | 0.1595 | 0.1783 | 0.1689 | | 0.3149 | 2.0 | 50... | b89c978513b72dbc116e05be81a76059 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/msmarco-distilbert-base-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 2f6714d2525c2c480cb936dd5609cc75 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 5a07ce6039b9f4c954f87a759721f408 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-distilbert-base-v3') model = AutoModel.from_pretrained('sentence-transformers/msmarco-distilbert-base-v3') | dc2a1693de3ac071d35901011d0d9ad4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-v3) | a804c4e82d4b41eedb8d8a72aead9e75 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 510, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mo... | 0ecdf4bc187d98eac0c8348ee956c74a |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-multilingual-uncased-label_sexist This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4966 - F1: 0.7819 | a9d22bf03065c6db34c544085a632ec4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4091 | 1.14 | 400 | 0.3794 | 0.7781 | | 0.2892 | 2.29 | 800 | 0.4185 | 0.7787 | | 0.2159 | 3.43 | 1200 | 0.4611 | 0.7906 | |... | eae5594a12566b5d002637b9371a7a33 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7647 - Matthews Correlation: 0.5167 | 907b4d039f10254b7fa46954ed080b46 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5294 | 1.0 | 535 | 0.5029 | 0.4356 | | 0.3507 | 2.0 | 1070 | 0.5285 | 0.4884 | | 0.2... | 9cc027b6dec2d5bd6531f6940a8b9d81 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_mrpc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5187 - Accuracy: 0.3309 - F1: 0.0683 - Combined Score: 0.1996 | 37c8a5d7922c22a479c88a38c9fa9c1f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.58 | 1.0 | 15 | 0.5281 | 0.3162 | 0.0 | 0.1581 | | 0.5287 | 2.0 | 30 | 0.52... | 5661f0e8562c36d1883b32cfdb32a31c |
mit | ['generated_from_trainer'] | false | finetuned-xlm-r-masakhaner-swa-whole-word-phonetic 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: 11.0418 | ed25fa64425dd28a4447385358f3cf37 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-08 - 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 - num_epochs: 500 | 322bae62ff2bcdf8522f85cbb80ffbe6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 62 | 39.5330 | | No log | 2.0 | 124 | 39.2832 | | No log | 3.0 | 186 | 39.7275 | | No log | 4.0 | 248 | 38.7389... | 04b72a1d4bc9f738bbf45453c049f085 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squad-qg-default` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/as... | 8cb84750191f72b3fa8300fe82489010 |
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", "research-backup/bart-large-squad-... | 906102214b85eb3072afb81328c6ef7e |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squad-qg-default/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 0719e0a82f9f969b6080d69a93d6770d |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 10 ... | b3fb342727565e3c9cfbae9860995097 |
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