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 hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precis... | f1e19f1cbffd80f684b5ff5ec45169e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.293 | 0.64 | 500 | 0.3798 | 99.9451 | | 0.1701 | 1.28 | 1000 | 0.3376 | 100.0 | | 0.1392 | 1.92 | 1500 | 0.3280 | 100.0 ... | bd3a7e98d9c598b1c1146a4575fd7670 |
mit | ['generated_from_trainer'] | false | roberta-offensive-lm-tapt-finetuned This model is a fine-tuned version of [k4black/roberta-offensive-lm-tapt](https://huggingface.co/k4black/roberta-offensive-lm-tapt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4692 - F1: 0.7744 | 72defc34b7713e670b8eaf4b392dcf99 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP | f54fa4319aa3a28eee60d9edf24f1aaf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6149 | 0.1 | 100 | 0.6323 | 0.3932 | | 0.5713 | 0.2 | 200 | 0.6223 | 0.5491 | | 0.5529 | 0.29 | 300 | 0.5739 | 0.6120 | |... | 14e7958b186d4e79b89c7e11ee29e5ac |
apache-2.0 | ['generated_from_keras_callback'] | false | kimhieu/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: 0.1828 - Validation Loss: 0.5520 - Train Matthews Correlation: 0.... | 71b953726f2d36d849395b16416261e6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5184 | 0.4675 | 0.4484 | 0 | | 0.3164 | 0.4646 | 0.4963 | 1 | | 0.1828 | 0.5520... | 55e93c1d7ad2e6f25f6b429b2eae9f94 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2085 - Accuracy: 0.9275 - F1: 0.9275 | 2a7df0a3d72842f4e3a62c05b74b5409 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8208 | 1.0 | 250 | 0.2989 | 0.9105 | 0.9085 | | 0.2418 | 2.0 | 500 | 0.2085 | 0.9275 | 0.9275 | | e0b7e5dc2501e79656998cdfe80d76ee |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_wavlm_s213 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | bdbee591106af97194bde83bc4e77ee4 |
apache-2.0 | [] | false | MobileNet V2 model from Torchvision fine-tuned for FOOD101 dataset. Checkpoint trained for 30 epoches using https://github.com/AlexKoff88/mobilenetv2_food101. Top-1 accuracy is 76.3% but one can do better. The main intend is to use it in samples and demos for model optimization. Here is the advantages: - FOOD101 can... | 3daed11b4d391e84ffdeb1d864c94c9e |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 24 - eval_batch_size: 4 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gam... | 81f855f06559853dde58027a2da767a6 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech-ml_s23 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | 2542468aaddeeae2343aa5cc00f91e6d |
apache-2.0 | ['generated_from_trainer'] | false | distil-Is-upper This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6095 - Rmse: 0.7807 - Mse: 0.6095 - Mae: 0.5993 | 13a64d8aada2ef5bc7b8207a199eca4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.7129 | 1.0 | 492 | 0.7088 | 0.8419 | 0.7088 | 0.5968 | | 0.5953 | 2.0 | 984 | 0.6426 | 0.8016 | 0.6426 ... | b2fe58d29870c7d22b4700f4757c2f26 |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test ``` <!-- Generated by scripts/utils/show_diar_result.sh --> | 565f8b9c5706999b30324936906d896e |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Environments - date: `Wed Aug 25 23:29:07 EDT 2021` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.2a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92` - Commit date: `Tue Aug 24 19:50:44 2021 -0400` | 350fd5428a3caa0ecc73a380dea7c15e |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DER `dev_clean_2_ns2_beta2_500` |threshold_median_collar|DER| |---|---| |result_th0.3_med1_collar0.0|32.42| |result_th0.3_med11_collar0.0|32.03| |result_th0.4_med1_collar0.0|30.96| |result_th0.4_med11_collar0.0|30.26| |result_th0.5_med1_collar0.0|30.35| |result_th0.5_med11_collar0.0|29.37| |result_th0.6_med1_collar0.... | 0873c4d388dc51e1e58c63cf7473d41c |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DIAR config <details><summary>expand</summary> ``` config: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw_max_epoch20 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist... | a8facf54aae769f029a3517c0f60ec64 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2201 - Accuracy: 0.9275 - F1: 0.9275 | 98f621bcc1846fc6a0da1146eec00127 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8326 | 1.0 | 250 | 0.3185 | 0.902 | 0.8983 | | 0.2499 | 2.0 | 500 | 0.2201 | 0.9275 | 0.9275 | | 7e5d48009698b0435c4f6737e9612008 |
creativeml-openrail-m | ['text-to-image'] | false | kemar Dreambooth model trained by zigg-ai with with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures ... | e1c6d64ac10a85254846a191ff9d90d1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/swbd_e_branchformer` This model was trained by Yifan Peng using swbd recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectur... | 6c560c261b47c8c980f0539c989e3de6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout ee573bc6f5de4309c1e29137294a7305d9175e65 pip install -e . cd egs2/swbd/asr1 ./run.sh --skip_data_prep false --skip_train true -... | 2985cdddb6e6f87e28d6976cf5364387 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue Dec 27 05:05:40 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `ef3ce328551c12c03284defc757f42df47c46170` - Commit date: `Mon Dec 26 20:34:28 2022 -0500` | a668ad4527630e50170984ce9f4f724e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/eval2000/hyp.callhm.ctm.filt.sys|2628|21594|88.7|8.4|2.9|2.1|13.4|46.2| |decode_asr_asr_model_valid.acc.ave/eval2000/hyp.ctm.filt.sys|4459|42989|91.2|6.1|2.8|1.5|10.4|41.5| |decode_asr_asr_model_... | f4b30a8f59891f9e4fa7e1ded0df2c61 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron_raw_en_bpe2000_sp n... | 39bfea4f334437b9308b40b69e861242 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-glue-cola-linear-probe 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: 0.6182 - Matthews Correlation: 0.0 | 35f1f3113f78a795ed64f40119c1e270 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6219 | 1.87 | 500 | 0.6194 | 0.0 | | 0.6094 | 3.73 | 1000 | 0.6188 | 0.0 | | 0.6... | c4c0e9ab096278da9822fa1aa1b4da73 |
apache-2.0 | ['generated_from_trainer'] | false | juancopi81/whisper-medium-es-train-valid This model is a fine-tuned version of [juancopi81/whisper-medium-es-train-valid](https://huggingface.co/juancopi81/whisper-medium-es-train-valid) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2227 - Wer: 6.1548 Using the ... | dfbe5df8df20a652a9f51c9e2b96dc0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0539 | 1.01 | 1000 | 0.2100 | 6.4465 | | 0.0211 | 2.01 | 2000 | 0.2286 | 6.5082 | | 0.0088 | 3.02 | 3000 | 0.2418 | 6.3848 | |... | e2fd3c9d1854d03f078fcf06343499f7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-irish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.4286 - Wer: 0.5097 | 2d2c800e36ff9ff8a147ce1cf067fbba |
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: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 4d05e0954b5922bbe36735016537ae9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.3406 | 24.97 | 400 | 1.1677 | 0.7270 | | 0.2527 | 49.97 | 800 | 1.2686 | 0.5927 | | 0.0797 | 74.97 | 1200 | 1.3970 | 0.576... | 74079b23f95c6497e2d7eaeb585965f4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | e97cf572079e73029505c06b86574ae7 |
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", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_... | 059fddb2114d2d4dc34f6fa9eddd1c2e |
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): \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"... | 4ec7bb28af17c1007454f88f704e40c8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish 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", "tr", split="test") we... | 11037556217c2b999edb23c24aa0b460 |
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 test_dat... | 50a1eebb51f072144a0e3a0cc8c7ffd3 |
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... | 7ec859327ea088713d9622c793d56c9b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1hesw9z_kFFINT93jBvGuFspOLrHx10AE?usp=sharing) | 3cb296911e9ced6ca013bfd9c9d746f7 |
mit | [] | false | nixeu on Stable Diffusion This is the `<nixeu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train you... | c0ae43391282a151ad5d28320fd05258 |
mit | [] | false | model by Pinguin This your the Stable Diffusion model fine-tuned the a hat in time girl concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a render of sks ** You can also train your own concepts and upload them to the library by using [this notebook](https://colab... | df24d8ca5cd9af98ec69be75c6a5bcea |
mit | [] | false | Spanish truecasing model This is a Spanish truecasing-model that works with the <b>Dalton Fury</b> Python project: https://github.com/daltonfury42/truecase You can install it here: https://pypi.org/project/truecase/ | 2890e336246172e0ab72c1bfc3197281 |
mit | [] | false | Quick start To use the Spanish model use the TrueCase.py file uploaded to this repository https://huggingface.co/HURIDOCS/spanish_truecasing/blob/main/TrueCaser.py Install the requirements: pip install nltk And ready to work: from TrueCaser import TrueCaser model_path = "spanish.dist" spanis... | ec79f29654493b97498836e9f3cacd22 |
mit | [] | false | Notes The model was trained with the Europarl dataset that contains transcriptions of the European Parliament discusions: https://www.statmt.org/europarl/ Europarl: A Parallel Corpus for Statistical Machine Translation, Philipp Koehn, MT Summit 2005 Using huggingface load_dataset: europarl = load_dataset('large_... | 20521dbf2da1d75fc919ae8f3fb55cdd |
apache-2.0 | [] | false | Arabic T5 Small Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-small` model, as it's much smaller and only targets Arabic and English based tasks. | f91f81fc04660d9e2ee6a03badfcba74 |
apache-2.0 | [] | false | About T5 ``` T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam S... | bfc55e9c8478ac94573219975e1c8f67 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0716 | 43a746e7d8f1d866b5318284e9edb909 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.5339 | 0.4 | 500 | 4.7224 | | 4.6477 | 0.8 | 1000 | 4.3242 | | 4.3146 | 1.2 | 1500 | 3.9988 | | 4.0046 | 1.6 | 2000 | 3.7777 ... | 09f4aea6be15a46284e0cab64500e279 |
cc-by-sa-4.0 | [] | false | Example *Use `Diffusers` >=0.8.0, do not support lower versions.* ```python from diffusers import StableDiffusionPipeline import torch model_path = "foldl/sd-rumeme-desc" pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt="кот").images[0] image... | f23c9bd6f65fc75f2a0ff966b82ae728 |
cc-by-sa-4.0 | [] | false | Training Procedure Model was trained on 1 P100 GPU for 10k steps. Base model - https://huggingface.co/OFA-Sys/small-stable-diffusion-v0 Training notebook here - https://www.kaggle.com/code/nukeee/meme-diffusion | b5f3516371f12dab24b46367206d4a23 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_gender_male-8_female-2_s226 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_0). When using t... | 61ea51dbaff27ca895c036d0c58cf84a |
apache-2.0 | [] | false | Graphcore/gpt2-small-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s... | b87197aa379214507e09c339cf55fbce |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the [GPT2 Small](https://huggingface.co/gpt2) model on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | d885ecf56e8dc3bb5453b92e3eb77891 |
apache-2.0 | ['generated_from_keras_callback'] | false | mzchua/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: 0.1940 - Validation Loss: 0.4943 - Train Matthews Correlation: 0.5... | 89081eb899a0eef1445aebc7f5c3241b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5156 | 0.4940 | 0.3942 | 0 | | 0.3226 | 0.4322 | 0.5448 | 1 | | 0.1940 | 0.4943... | d6a7f530e5f56b7721a9efebb9a7cba3 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4535 - Accuracy: 0.79... | fec5042768982bf5a51d4d72c36a877c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6748 | 0.24 | 500 | 0.6424 | 0.6468 | | 0.5996 | 0.48 | 1000 | 0.5542 | 0.7167 | | 0.5172 | 0.71 | 1500 | 0.5001 | 0.... | 40d31e9603fbbdebbf9ab420854dcd05 |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT-finetuned-resumes-sections This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0369 - F1: 0.9652 - Roc Auc: 0.9808 - Accuracy: 0.96... | c85ef35a4f365346f5ddbe2723645f10 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:| | 0.0509 | 1.0 | 1173 | 0.0331 | 0.9439 | 0.9659 | 0.9356 | | 0.024 | 2.0 | 2346 | 0.0274 | 0.9... | 82a27138327bf7141e1202429aa7d422 |
mit | ['generated_from_trainer'] | false | hmBERT-CoNLL-cp3 This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0572 - Precision: 0.9121 - Recall: 0.9243 - F1: 0.9182 -... | c7b4d36a3b02f5952347c4e84cf3e396 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.06 | 25 | 0.4115 | 0.3643 | 0.3728 | 0.3685 | 0.9007 | | No log | 0.11 |... | 7507680b6901aa6c12b01bd7256b8bce |
mit | ['generated_from_trainer'] | false | indobert-hoax-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 - Accuracy: 0.8059 | 6d8d0240542b8d42382892dc7b55762c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.2173070213315e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 30 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | af1eb8e200ac1a34af7a8904000a7c07 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 85 | 0.5540 | 0.7029 | | No log | 2.0 | 170 | 0.5432 | 0.7029 | | No log | 3.0 | 255 | 0.4963 | 0.... | 78ccdc0c172eb2222aa4f3c968b79dc6 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the mimica concept trained by mjfang27 on the mjfang27/dreambooth-hackathon-images dataset. This is a Stable Diffusion model fine-tuned on the mimica concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of mimica cat** This model was created as part of the DreamB... | e5dba81d507580a3c12199c7183d2a6b |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-bart-large-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | 5ed880e960365f601887f581a831bff6 |
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: 0.2202 - Accuracy: 0.925 - F1: 0.9252 | 4223182de4d87f0c4fb1443589b93df1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8419 | 1.0 | 250 | 0.3236 | 0.9025 | 0.8999 | | 0.258 | 2.0 | 500 | 0.2202 | 0.925 | 0.9252 | | 6305af9f0b89a9d8a34e2074b9ab337d |
mit | [] | false | agm-style on Stable Diffusion Artist: <https://www.pixiv.net/en/users/20670939> This is the `<agm-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conce... | a1bd3ea0c46de7c0828ceace9396be99 |
apache-2.0 | ['generated_from_keras_callback'] | false | KakkiDaisuki/bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0259 - Validation Loss: 0.0580 - Epoch: 2 | f94cf19f0bbedc1d056c495e2b88e6f0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1253 | 0.0569 | 0 | | 0.0417 | 0.0582 | 1 | | 0.0259 | 0.0580 | 2 | | 643efeec3b894d465bf6aee895e53352 |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2113 - Precision: 0.7349 - Recall: 0.7560 - F1: 0.7453 - Accuracy: 0.... | ac4c6276dda8877eadee8f1d7381a481 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 191 | 0.1914 | 0.7105 | 0.7181 | 0.7143 | 0.9382 | | No log | 2.0 |... | efa7bd7978751ef105a5add9bb76c50c |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Whisper Large French Cased This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: - Loss: 0.2962 - Wer: 11.9100 | ce0ec531906f0f8a39330dc96eac27a4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precisio... | bcf5d2d1cb3583b8605161adadbe9412 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3357 | 0.2 | 1000 | 0.3994 | 16.1523 | | 0.3026 | 0.4 | 2000 | 0.3802 | 15.2403 | | 0.2904 | 0.6 | 3000 | 0.3389 | 14.004... | f9e938789642f6d9f6af6abd2392bb30 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3436 - Accuracy: 0.5634 | 77ab6478ea8eb8aa5277381f8ebd603a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3511 | 1.0 | 3 | 0.3436 | 0.5634 | | 0.3479 | 2.0 | 6 | 0.3457 | 0.5634 | | 0.3474 | 3.0 | 9 | 0.3462 | 0.... | 654ebf2d5f414ff07178812d588b7604 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0492 - Precision: 0.9530 - Recall: 0.9604 - F1: 0.9567 - Accuracy: 0.9889 | cf3ca54a394fa9aa9ce9f9352c3fdee9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2031 | 1.0 | 878 | 0.0560 | 0.9381 | 0.9445 | 0.9413 | 0.9858 | | 0.0446 | 2.0 |... | 7aa8c46cd917539d80d418b92ca8b6fd |
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: 0.0858 - Precition: 0.9363 - Recall: 0.9522 - F1: 0.9442 - Accuracy: 0.9866 | 7a24e0d995ec0ef17b3fc545118822ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precition | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0081 | 1.0 | 1756 | 0.0914 | 0.9273 | 0.9446 | 0.9359 | 0.9848 | | 0.012 | 2.0 |... | a341161159babb9841004bd08b8028dc |
mit | ['generated_from_trainer'] | false | hungry_saha This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorba... | 54a23d5d8c57146f49ffe4bdc8d2909e |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | 197ae1170c013251588c01ce2a12ea0b |
apache-2.0 | ['generated_from_keras_callback'] | false | prahlad/rotten_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tomatoes movie review dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4876 - Train Accuracy: 0.7620 - Validation Loss: 0.5001 - Validation Accuracy: 0.78... | d613ac2e048187b4e8a64f3e587c5666 |
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': 5e-05, 'decay_steps': 12795, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | 0daa1ad5bf7b3650b15cc1585c1a536f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4876 | 0.7620 | 0.5001 | 0.7842 | 0 | | 81fffc2a7ce6af4099fcbd15a1203c4c |
cc-by-4.0 | [] | false | Danish ELECTRA small (cased) An [ELECTRA](https://arxiv.org/abs/2003.10555) model pretrained on a custom Danish corpus (~17.5gb). For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers/tree/main/electra | e3a88ca74ff2b9ba172d1e74e304dcf4 |
cc-by-4.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarnikowski/electra-small-generator-da-256-cased") model = AutoModel.from_pretrained("sarnikowski/electra-small-generator-da-256-cased") ``` | 70cd7c5ce6fddb29dd1513e0f6184a24 |
cc-by-4.0 | [] | false | Questions? If you have any questions feel free to open an issue in the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com | 782946cea92d2a45db0f7195a12012ea |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_20k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different r... | 87f10ac689a5969def7f3cbf96c7670b |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_20k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_20k') model = TFBertModel.from_pretrained("google/multiber... | ea77224b8ffc30b6032ce662e6a385c5 |
mit | ['generated_from_trainer'] | false | label-transfer This model is a fine-tuned version of [saattrupdan/verdict-classifier](https://huggingface.co/saattrupdan/verdict-classifier) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0452 - F1 Macro: 0.9872 - F1 Misinformation: 0.9918 - F1 Factual: 0.9979 - F1 Other: 0.97... | d15da288c83e6e010316c9b0156938f8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 1c9063c4d2bedb937217495ef26c0847 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------... | f5f2228c810d76f43b36a8278655b358 |
apache-2.0 | ['translation'] | false | opus-mt-ase-es * source languages: ase * target languages: es * OPUS readme: [ase-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | ffa5b41cc43fd698dd5abce3ede5ca85 |
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: 0.0590 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9867 | 962698a7fa0f01387d844d23a708f8ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0872 | 1.0 | 1756 | 0.0709 | 0.9194 | 0.9334 | 0.9263 | 0.9822 | | 0.033 | 2.0 |... | 10680e0fdcc24bc9ededd2e459382676 |
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