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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.538 | 1.0 | 535 | 0.5812 | 0.3250 | | 0.3669 | 2.0 | 1070 | 0.5216 | 0.4993 | | 0.2... | b9732cfe11abbaebfb5097760d34c9ff |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1422 - Wer: 35.2207 | 89574e938f02b839d27db6e5d3482098 |
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: 16 - 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: 500 - training_steps: 7000 - mixed_precisi... | d17696c7cb3f822fa6fc6a943b17523a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1137 | 4.02 | 1000 | 0.9072 | 40.0987 | | 0.0153 | 9.02 | 2000 | 1.0351 | 38.7631 | | 0.0042 | 14.01 | 3000 | 1.0507 | 36.440... | 3db912c30076e1fa41851ce0f1fdb04c |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4638 - Accuracy:... | 142ec02d1a54f6cbffc9d1f7d76f22a0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4069 | 0.7976 | 0.875 | | No log | 2.0 | 390 | 0.4061 | 0.8134 | 0.8838 | | 0.4074 |... | 6eda02ad7094ab89863a0598c58574e1 |
apache-2.0 | ['generated_from_trainer'] | false | rte_bert-base-uncased_144_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7639 - Accuracy: 0.6498 | 7702d1ca7c91d18311f63468caf88871 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.7555 - Rouge1: 11.8922 - Rouge2: 1.88 - Rougel: 9.6595 - Rougelsum: 10.8308 - Gen Len: 18.9911 | cf9174e03ac5786bd400f63771c8ff1f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 3.9546 | 1.0 | 34080 | 3.7555 | 11.8922 | 1.88 | 9.6595 | 10.8308 | 18.99... | b66eba734b9e8f35d8b69770b14dd79e |
mit | ['generated_from_trainer'] | false | run-4 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6296 - Accuracy: 0.685 - Precision: 0.6248 - Recall: 0.6164 - F1: 0.6188 | db3dfc136af3cbe79486aff7a5cf1d13 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - num_epochs: 20 | b326e01239d62c9dd341024a8d86d251 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0195 | 1.0 | 50 | 0.8393 | 0.615 | 0.4126 | 0.5619 | 0.4606 | | 0.7594 | 2.0 |... | 57d9d3d8b80af7b7dbf89907f7597d7a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b pip install -e . cd egs2/commonvoice/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/pt_commonvoice_blstm ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 278de1b5242c59791412ac8b6d5d179d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Apr 11 18:55:23 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b` - Commit date: `Mon Apr 4 21:04:45 2022 -0400` | d40736dfda8ca8e9e2fb669746a99d95 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_rnn.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_rnn_raw_pt_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_... | 9e399e68f6fa189f8471fa8d034a0c46 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum-ss This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5823 - Rouge1: 26.3663 - Rouge2: 6.4727 - Rougel: 20.538 - Rougelsum: 20.5411 - Gen Len: 18.8006 | d59cbd36d6222b65e816fec0b8967349 |
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: 0.25 - mixed_precision_training: Native AMP | 66ab49c5621fdefab2b6e68a4cde9a1d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.8125 | 0.25 | 3189 | 2.5823 | 26.3663 | 6.4727 | 20.538 | 20.5411 | 18.8006 ... | ebdb2f45cb63196c4fa00d55222030dc |
bsd-3-clause | ['codegen', 'text generation', 'pytorch', 'causal-lm'] | false | Overview The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research. The abstract from the paper is the following: Program synthesis strives to generate a computer program as a solution to a given problem ... | bca8ba94ee1e47b6652627b904ce8d26 |
bsd-3-clause | ['codegen', 'text generation', 'pytorch', 'causal-lm'] | false | Usage `trust_remote_code` is needed because the [torch modules](https://github.com/salesforce/CodeGen/tree/main/jaxformer/hf/codegen) for the custom codegen model is bundled. ```sh from transformers import AutoModelForCausalLM, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained(model_folder, local_files_only=T... | 984efedfee2ce482088b0ba032206238 |
mit | ['generated_from_trainer'] | false | hasoc19-xlm-roberta-base-targinsult1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7512 - Accuracy: 0.7096 - Precision: 0.6720 - Recall: 0.6675 - F1: 0.6695 | 1805354581870b894f3840e205921a41 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 263 | 0.5619 | 0.6996 | 0.6660 | 0.6717 | 0.6684 | | 0.5931 | 2.0 |... | aa6a84fc897733c7bac40d378b3159ab |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-v3-e4 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7934 - Rouge1: 54.2624 - Rouge2: 35.6024 - Rougel: 37.1697 - Rouge... | d64f6e35b5f38dd5ba77573043b484ca |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9533 | 52.3191 | 32.4576 | 33.2016 | 49.6502 | ... | 9247dead67bb27760d9413c11fcb9c36 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0703 - Accuracy: 0.9770 | 7c9e3140aef533248fa9beb99ce59123 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2369 | 1.0 | 190 | 0.1683 | 0.9433 | | 0.1812 | 2.0 | 380 | 0.0972 | 0.9670 | | 0.1246 | 3.0 | 570 | 0.0703 | 0.... | 4c9dbfcf120f5c9064435a96267de632 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Fongbe dataset within SpeechBrain. For a better experience, we encourage y... | acc177fec6076c5f270d26b4b1a59f74 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions. - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/f... | 7b9f8448bad66d2a62c2cdf860ad97da |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Install SpeechBrain First of all, please install transformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read the SpeechBrain tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | 3ea25305fe1d15922ee9df2a9cb36c20 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Fongbe) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-fongbe", savedir="pretrained_models/asr-wav2vec2-dvoice-fongbe") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-fongbe/example_fo... | 68dd174f9cdb24629717d38432af1aeb |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Training The model was trained with SpeechBrain. 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: ```bash cd recipes/DVoice/ASR... | 4e42649d36b5eda4017989cac3549da2 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | About DVoice DVoice is a community initiative that aims to provide African low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are current... | e5b092f1a9a8ebd6cbd79b4c200588f1 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | About AIOX Labs Based in Rabat, London, and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies. - He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience. - AIOX-Labs is multi-sect... | 5eff50e0b58f424fa63d851aa8752083 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | SI2M Laboratory The Information Systems, Intelligent Systems, and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial In... | d87e43ccdc431be96e65175845f376de |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in German using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | ed8b65ceb0cbcccb4bab3dcd93ab0883 |
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", "de", split="test[:2%]") processor = Wav2Vec2Processor.fr... | d020a23a6d878c9ff7749675a1571d48 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speec... | bee3d0a16a0cab531e301caebd314270 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Czech 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", "de", split="test[:15... | 6cd80209f6a99b4166a80c36af1ce042 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch ... | 1fb06ff7a397c4a422a70971879bd696 |
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 ... | b379895ef5fdcfc0783e74951ad09542 |
apache-2.0 | ['translation'] | false | opus-mt-crs-fr * source languages: crs * target languages: fr * OPUS readme: [crs-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/crs-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | f42bd399a9e3275d603a09d2a8a238fa |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco', 'classification'] | false | Training data The model initialized from [`ai-soco-c++-roberta-tiny`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification. | a9c5b60e634b72aaa0cd6f2e4c85e1d8 |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco', 'classification'] | false | Training procedure The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization. | 5af0b4fb084d2e76580edd2508bae802 |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco', 'classification'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ai-soco-2020-fire, title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}", author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and ... | f4a8f90ab5dff0dad893d2040deeac18 |
apache-2.0 | ['generated_from_trainer'] | false | whispQuote-ChunkDQ-DistilBERT This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2582 - Precision: 0.5816 - Recall: 0.8129 - F1: 0.6780 - Accuracy: 0.9126 | 012cc605e4373a345eda2120f7656df2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 164 | 0.3432 | 0.4477 | 0.5795 | 0.5052 | 0.8796 | | No log | 2.0 |... | b17199e05647b7878abe0376265b452f |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetune This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5677 - Rouge1: 9.9893 - Rouge2: 5.2818 - Rougel: 9.7766 - Rougelsum: 9.7951 - Gen Len: 58.1672... | 6984bb3ddcce6499e9effba5b886ebd6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.2639 | 1.0 | 4774 | 1.5677 | 9.9893 | 5.2818 | 9.7766 | 9.7951 | 58.1672 | ... | 662cfe4af378184f4fb6a270dc376d43 |
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: 0.7643 - Matthews Correlation: 0.5291 | 1912d35dd13e478491aecc6e766f1a8e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5288 | 1.0 | 535 | 0.5111 | 0.4154 | | 0.3546 | 2.0 | 1070 | 0.5285 | 0.4887 | | 0.2... | 2bdc8d82ed177edf271d3af8a4852009 |
apache-2.0 | ['generated_from_trainer'] | false | M7_MLM_final This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.4732 | 01543a778264da6a8a6b97acbc612079 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.769 | 1.0 | 92 | 6.6861 | | 6.3549 | 2.0 | 184 | 5.7455 | | 5.826 | 3.0 | 276 | 5.5610 | | f85e7edabc62239c17c755db24a85b8d |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech-ml_s418 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | 699a278cd40945393ecdf82ac83013a7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ie 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: - eval_loss: 1.5355 - eval_accuracy: 0.4318 - eval_runtime: 111.662 - eval_samples_per_second: 17.983 -... | cebc6c512120d94738d0720582a55b48 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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... | 8f2f1efbb5141d0170ff35c19eea1ecb |
apache-2.0 | [] | false | distilbert-base-el-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 accuracy... | 5e26e6d954ecc3b5c71000312060f986 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-el-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-el-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 84bc4c6f192478945e9d7a46a0045857 |
apache-2.0 | ['generated_from_keras_callback'] | false | Jaspal/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1904 - Validation Loss: 0.5593 - Train Matthews Correlation: 0.5... | c258dd6f51e539b59c03f12cb8c64917 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 4332d1a3cb803a81a1c3d3887a635722 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5175 | 0.4542 | 0.4684 | 0 | | 0.3255 | 0.4617 | 0.5007 | 1 | | 0.1904 | 0.5593... | 4d532c2390170fc436a4963e0cea4a37 |
apache-2.0 | ['translation'] | false | opus-mt-en-kg * source languages: en * target languages: kg * OPUS readme: [en-kg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-kg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 84a39880d44f3fb59d99e6c3185d5711 |
creativeml-openrail-m | ['text-to-image'] | false | Wave Concepts Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/no... | 871d54579f51c953c2218f129bc3f116 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_tiny_tf_384.in1k An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | b27ab9f6d036066d9618aa711b4772fe |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 31.0 - GMACs: 17.5 - Activations (M): 123.4 - Image size: 384 x 384 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 93c8a49ce1000b38bef6a1700a302e68 |
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('maxvit_tiny_tf_384.in1k', pretrained=True) model = mod... | 5515a5c45c91c29d7814a64318c641a5 |
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( 'maxvit_tiny_tf_384.in1k', pretrained=True, ... | 0970410ec2233d83972014c3cc772e9a |
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( 'maxvit_tiny_tf_384.in1k', pretrained=True, nu... | b3e8662d62ce91f2c6c139ea80fae323 |
apache-2.0 | ['translation'] | false | opus-mt-pon-fi * source languages: pon * target languages: fi * OPUS readme: [pon-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pon-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 87e1a3b98c4338f2b9ef7a7c614708dc |
apache-2.0 | ['translation'] | false | ukr-nld * source group: Ukrainian * target group: Dutch * OPUS readme: [ukr-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nld/README.md) * model: transformer-align * source language(s): ukr * target language(s): nld * model: transformer-align * pre-processing: normalization + Sent... | 716c068ff151b71a6279a0bf66d9f202 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-nld - source_languages: ukr - target_languages: nld - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nld/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'nl'] - src_constituents: {'ukr'} - tgt_const... | f7d617920bbaf0595e39bcc0e6e5c7c4 |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | deployment-with-nvidia-riva) | FastPitch [1] is a fully-parallel transformer architecture with prosody control over pitch and individual phoneme duration. Additionally, it uses an unsupervised speech-text aligner [2]. See the [model architecture]( | 16a2ef8cedea97efa3bde67591c0676d |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've ins... | 9037ba6c72abedd7736be23d5c80fa0f |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | Generate audio ```python import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") spectrogram = spec_generator.generate_spectrogram(tokens=parsed) audio = model.convert_spectrogram_to_audio(spec=spectrogram) ``` | f41b1621dfeea4c59b6242caa6aef52a |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | Model Architecture FastPitch is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in t... | 5fe9be115c809fe2e41f0a3e4bd35178 |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | Training The NeMo toolkit [3] was used for training the models for 1000 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/fastpitch.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/fastpitch_align_v1.05.yaml). | eabe32280434d9666d3e17c1edff4d41 |
cc-by-4.0 | ['text-to-speech', 'speech', 'audio', 'Transformer', 'pytorch', 'NeMo', 'Riva'] | false | References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) | d656861b19f21e39350f8c30cfb96a5d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-squad-plain_text This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0170 | 8c1f216496b0ce409109a4f2264401d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.4628 | 0.4 | 500 | 3.9931 | | 4.0687 | 0.8 | 1000 | 3.9571 | | 3.9256 | 1.2 | 1500 | 3.9381 | | 3.7901 | 1.6 | 2000 | 3.9680 ... | bed9f0dd556dc71925bbc636524929ab |
apache-2.0 | ['exbert'] | false | BERT base model (cased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference betwe... | 4a54f964421442411310bc8b5540b747 |
apache-2.0 | ['exbert'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-cased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] Hello I'm a fashion model. [SEP]", 'score': 0.090191... | ecbcea876930d469a7824341916ceac4 |
apache-2.0 | ['exbert'] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-cased') >>> unmasker("The man worked as a [MASK].") [{'sequence':... | 2a659cd7669b0671a9306544c08dea9f |
mit | ['generated_from_trainer'] | false | test-conll2003-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0470 - Precision: 0.9459 - Recall: 0.9537 - F1: 0.9498 - Accuracy: 0.9911 | e9a989da54a350818c2f3c307769ec4a |
mit | [] | false | Usage - `pip install -U bnlp_toolkit` - Generate Vector using pretrain model ```py from bnlp import BengaliWord2Vec bwv = BengaliWord2Vec() model_path = "bengali_word2vec.model" word = 'গ্রাম' vector = bwv.generate_word_vector(model_path, word) print(vector.shape) print(vector) `... | 3a86509e11a934c1aa36fe18e1215152 |
mit | [] | false | dtv-pkmn on Stable Diffusion This is the `<dtv-pkm2>` 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 tra... | 3e95b90a3f7f212291ce149b956632b6 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Norwegian (nb) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](h... | c7f609d4d1659ea29bbf6b798da87328 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | gtsrb-model 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 bazyl/GTSRB dataset. It achieves the following results on the evaluation set: - Loss: 0.0034 - Accuracy: 0.9993 | d407fee56809ea784dca0734f109698c |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 1ffd12ee38529d83052063c9b2929892 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2593 | 1.0 | 4166 | 0.1585 | 0.9697 | | 0.2659 | 2.0 | 8332 | 0.0472 | 0.9900 | | 0.2825 | 3.0 | 12498 | 0.0155 ... | 2fb4090aa71474981ba011cededd9f94 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_sst2 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4157 - Accuracy: 0.8028 | acdbc8bd5cf404fc330b3e769aa608f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_tra... | 951e85de781ec422657e4a09723ca9e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.487 | 1.0 | 527 | 0.4157 | 0.8028 | | 0.2824 | 2.0 | 1054 | 0.4351 | 0.8005 | | 0.2265 | 3.0 | 1581 | 0.4487 | 0.... | 0981fd106391df9676bcee5389dd495b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/microsoft-fluentui-emoji-512-whitebg](https://huggingface.co/datasets/Norod78/microsoft-fluentui-emoji... | 28092c7e18c42f0a63293afa0c27b22e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | The Emoji file names were converted to become the text descriptions. It made the model learn a few special words: "flat", "high contrast" and "color"  | 43696cc0131b593cc35d4b3ed91be872 |
cc | ['pos'] | false | POS tagger based on SlovakBERT This is a POS tagger based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model uses [Universal POS tagset (UPOS)](https://universaldependencies.org/u/pos/). The model was fine-tuned using Slovak part of [Universal Dependencies dataset](https://universaldependencies.or... | 41c9bf57d0a6c6cf2c373daa4daf68f8 |
cc | ['pos'] | false | Cite ``` @article{DBLP:journals/corr/abs-2109-15254, author = {Mat{\'{u}}{\v{s}} Pikuliak and {\v{S}}tefan Grivalsk{\'{y}} and Martin Kon{\^{o}}pka and Miroslav Bl{\v{s}}t{\'{a}}k and Martin Tamajka and Viktor Bachrat{\'{y}} and ... | 5dede72b23c88b4dab74eff8a5d81171 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-medical This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2214 - Wer: 0.0975 | f6e6e515b8c61e2228fa87adb179e657 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.0393 | 2.47 | 200 | 3.2401 | 1.0 | | 2.8825 | 4.94 | 400 | 1.0054 | 0.8592 | | 0.4256 | 7.41 | 600 | 0.2495 | 0.2448 | |... | b31959f75459740c9d9a7af61e258526 |
creativeml-openrail-m | ['text-to-image'] | false | model by maxnadeau This your the Stable Diffusion model fine-tuned the Colorful ball concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks ball** You can also train your own concepts and upload them to the library by using [this notebook](https://colab... | 29688ed149e497f47c6d40b0bdd24156 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-bert-base-max-pooling 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. | b0e4a6c8ecda920b9dbfbb36f901c0b6 |
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... | 823c9bfe48e17a09fe7154c3b9abe48e |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.