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 | ['Noe tags', 'generated_from_trainer'] | false | Whisper Small spanish - Sanchit Gandhi notebook example This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the small random dataset dataset. | 91b2451be7a22c2b47c586f0f4ff0665 |
apache-2.0 | ['Noe tags', '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: 7 | 280dfba9e6ac5786497237c9ade1c128 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-small-0505 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: - Loss: 1.8649 - Accuracy: 0.1818 - F1: 0.1182 | 491e8bbb867d64a423bf2925ece74759 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 13 | 1.8337 | 0.1818 | 0.0559 | | No log | 2.0 | 26 | 1.8559 | 0.2727 | 0.1414 | | No log |... | 7eac4ac6cb978f6dd7071b458a7ababb |
apache-2.0 | [] | false | distilbert-base-en-es-pt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original ac... | 40c389f72f8a9548d90d805b126b439b |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-es-pt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-es-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | 67b9e16e6bef3f6adf3e8b233da3d262 |
apache-2.0 | ['automatic-speech-recognition', 'BembaSpeech', 'generated_from_trainer'] | false | xls-r-300m-bemba-fullset This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the BEMBASPEECH - NYA dataset. It achieves the following results on the evaluation set: - Loss: 0.6071 - Wer: 0.9917 | 24663afdc122b7ecdf9f31fdb744190b |
apache-2.0 | ['automatic-speech-recognition', 'BembaSpeech', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 3494a9bc3cb359f627f1e88a65bacba3 |
apache-2.0 | ['automatic-speech-recognition', 'BembaSpeech', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.6564 | 1.58 | 500 | 0.6071 | 0.9917 | | 8ead9b5b0313ff0ccf6a4fa8c8e7b3a0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Wer: 0.4880 | c567b8da511c410bc8d39644b14b74ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2135 | 4.21 | 400 | 2.5232 | 1.0 | | 0.8323 | 8.42 | 800 | 0.4673 | 0.6142 | | 0.3247 | 12.63 | 1200 | 0.4087 | 0.5536 | |... | 053ab7e77959b3772cbfb3aca7c3aca6 |
mit | ['generated_from_keras_callback'] | false | nouman10/robertabase-claims-3 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: - Train Loss: 0.0310 - Validation Loss: 0.1227 - Epoch: 1 | ea8c3a16db5e32499640354a828fc676 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 5bba338a01bdc56bfaa8381a236ef3bd |
apache-2.0 | [] | false | This model receives scrambled or incoherent sentences as input and returns a meaningful sentence using the same words in the input . A form of grammar correction if you may . It was trained on a dataset of permutated sentences derived from wikipedia pages as input with the correct arrangement of words as labels . It... | 50d4fa885ab77a6137327c76183149ed |
apache-2.0 | ['roberta-wwm'] | false | 模型&下载 * `base模型`：12-layer, 768-hidden, 12-heads, 110M parameters | 模型简称 | 京盘下载 | | :----: | :----:| | fin-roberta-wwm | [Tensorflow](https://3.cn/103c-hwSS)/[Pytorch](https://3.cn/103c-izpe) | | fin-roberta-wwm-large | todo | | d8447364898d4ba7b3b0bc791a71caf9 |
apache-2.0 | ['roberta-wwm'] | false | 快速加载 依托于[Huggingface-Transformers](https://github.com/huggingface/transformers)，可轻松调用以上模型。 ``` tokenizer = BertTokenizer.from_pretrained("MODEL_NAME") model = BertModel.from_pretrained("MODEL_NAME") ``` **注意：本目录中的所有模型均使用BertTokenizer以及BertModel加载，请勿使用RobertaTokenizer/RobertaModel！** 其中`MODEL_NAME`对应列表如下： | 模型名 | MODE... | b0ee2cf4b84796a062f41c899d30161c |
apache-2.0 | ['roberta-wwm'] | false | 任务效果 | Task | NER | 关系抽取 | 事件抽取 | 指标抽取 | 实体链接 | |:----:|:-- :|:------:|:-------:|:-------:|:------:| | Our |93.88| 79.02 | 91.99 | 94.28| 86.72 | | Roberta-wwm |93.47| 76.99 | 91.58 | 93.98| 85.20 | | 527ccf21fb397ac455b9f8513befce33 |
apache-2.0 | ['generated_from_trainer'] | false | pft-clf-finetuned This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0987 - Matthews Correlation: 0.9737 | 3e5a293fd4c787eb5ddc8d01b25c4141 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 6 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | a9625a2a7841dd175386b45ae2f221d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.1299 | 1.0 | 1268 | 0.0987 | 0.9737 | | e7b695c70ab82e61129c4bb35d8c14f2 |
apache-2.0 | ['generated_from_keras_callback'] | false | laxsvips/my_laxs_first_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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5556 - Train Accuracy: 0.4339 - Validation Loss: 0.3070 - Val... | 629d7cc95388066b410d49d8197695df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 74470, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 3a055b283aafc4a9981aa1e48a3e3618 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.5556 | 0.4339 | 0.3070 | 0.5604 | 0 | | f7ec11434308e9bd6a7e344ad7e7a240 |
apache-2.0 | Text Classification | false | BatterySciBERT-uncased for Battery Abstract Classification
**Language model:** batteryscibert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastr... | 02c1a4069ec77824836e0d8ebd45ee02 |
mit | ['generated_from_trainer'] | false | ecstatic_jepsen 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 tomekk... | 314275f62c8eebf2695248d45559c920 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 1f3caef04647c7065c8bc905b6ebb7a8 |
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: 0.2198 - Accuracy: 0.924 - F1: 0.9239 | 0080256e35c4ba7d008588ca27fb77c6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3189 | 0.907 | 0.9052 | | 0.2468 | 2.0 | 500 | 0.2198 | 0.924 | 0.9239 | | 2ac3ee35dabe8ebb0200f93188070e44 |
apache-2.0 | ['translation'] | false | opus-mt-en-cs * source languages: en * target languages: cs * OPUS readme: [en-cs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-cs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | edbedc3f6df3ca59b0ac1c11a58e9b2c |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.en.cs | 22.8 | 0.507 | | news-test2008.en.cs | 20.7 | 0.485 | | newstest2009.en.cs | 21.8 | 0.500 | | newstest2010.en.cs | 22.1 | 0.505 | | newstest2011.en.cs | 23.2 | 0.507 | | newstest2012.en.c... | 4780bedf42a963039c80041bf76137b3 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | How to use a LORA? Place it in "\stable-diffusion-webui\models\Lora". Don't see the folder? git pull Load any model, preferably an anime model, use the purple icon under the generate button to bring up the LORA list or just add it to your prompt like this <<lora:popn:1>>, change the number to reduce the strenght lik... | da6e9c69d8138e1779c97d591cd4e08b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | popn2.safetensors examples  ![01398-1463941104-1girl, solo, brown hair, ... | caa5e3b037b77f073f64001d18b6bc0a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors', 'lora'] | false | popn.safetensors examples  ![00889-492907965-1girl, solo, black hair, ... | 1a20d61b14fd79bb1d5b75c3bcc6efb1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-or-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9276 - Wer: 1.1042 | b45fd19aed65521f19549c6668c600fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.764 | 24.97 | 400 | 0.9276 | 1.1042 | | c073f7725de9c3bd97afbbf08865aa5b |
mit | ['generated_from_trainer'] | false | bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.1505 - Precision: 0.7854 - Recall: 0.8158 - F1: 0.... | 4319dc9373416588c5cfa74cde672a87 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0981 | 1.0 | 782 | 0.0712 | 0.7228 | 0.7948 | 0.7571 | 0.9724 | | 0.0509 | 2.0 |... | 2a20596d1b0c2d7ddb738ada2561fbdc |
mit | ['video-classification', 'videomae', 'vision'] | false | Model Description <!-- Provide a longer summary of what this model is. --> VideoMAE Base model fine tuned on UCF101 - **Developed by:** [@nateraw](https://huggingface.co/nateraw) - **Shared by [optional]:** [More Information Needed] - **Model type:** fine-tuned - **Language(s) (NLP):** en - **License:** mit - **Rel... | 41f851b70dab4bda687baf69f581038a |
mit | ['video-classification', 'videomae', 'vision'] | false | Preprocessing We sampled clips from the videos of 64 frames, then took a uniform sample of those frames to get 16 frame inputs for the model. During training, we used PyTorchVideo's [`MixVideo`](https://github.com/facebookresearch/pytorchvideo/blob/main/pytorchvideo/transforms/mix.py) to apply mixup/cutmix. | 63c8ffd09fb49712a999ada6cff6b80d |
mit | ['video-classification', 'videomae', 'vision'] | false | Results We only trained/evaluated one fold from the UCF101 annotations. Unlike in the VideoMAE paper, we did not perform inference over multiple crops/segments of validation videos, so the results are likely slightly lower than what you would get if you did that too. - Eval Accuracy: 0.758209764957428 - Eval Accurac... | 9521e6333c8dbd30dcb7b9def8084961 |
mit | ['video-classification', 'videomae', 'vision'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from decord import VideoReader, cpu import torch import numpy as np from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification from huggingface_hub imp... | 2734b0013fb4eb6029f0e2f01e80beb4 |
mit | ['video-classification', 'videomae', 'vision'] | false | video clip consists of 300 frames (10 seconds at 30 FPS) file_path = hf_hub_download( repo_id="nateraw/dino-clips", filename="archery.mp4", repo_type="space" ) videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) | 7a594c1408dc129fb2c9b5f4d01ff9b8 |
mit | ['video-classification', 'videomae', 'vision'] | false | sample 16 frames videoreader.seek(0) indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) video = videoreader.get_batch(indices).asnumpy() feature_extractor = VideoMAEFeatureExtractor.from_pretrained("nateraw/videomae-base-finetuned-ucf101") model = VideoMAEForVideoClassification... | 28e26987cbfac4ece1bfb8adc54c010c |
apache-2.0 | ['generated_from_trainer'] | false | token_fine_tunned_flipkart_2_gl6 This model is a fine-tuned version of [vinayak361/token_fine_tunned_flipkart_2_gl](https://huggingface.co/vinayak361/token_fine_tunned_flipkart_2_gl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7363 - Precision: 0.7243 - Recall: 0.7752 - F1:... | b85e6a2af2b748aecdd4b5d43782fc20 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - 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: 20 | 7863db5da584611b360f7daec958617e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 120 | 1.0057 | 0.6623 | 0.7212 | 0.6905 | 0.6995 | | No log | 2.0 |... | c71d8c94ddb254207cc8fe443bd2dd7a |
mit | ['generated_from_trainer'] | false | 2-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: 11.0492 | 0eb553f8706152a072e25c6b3b456d7b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-08 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | 52b560ecb9ef2ff38a3a8c2c17a97cc8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 61 | 39.7928 | | No log | 2.0 | 122 | 39.8195 | | No log | 3.0 | 183 | 39.8228 | | No log | 4.0 | 244 | 39.0793... | 16b666d52c7ee8ff4570c868f5c8ca0e |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Romanian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | ab36cab3f667fa9690acc777b08a0398 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ro") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ro") ``` | e6375584df88da4280ea47f494f9af87 |
apache-2.0 | ['translation'] | false | opus-mt-lus-fi * source languages: lus * target languages: fi * OPUS readme: [lus-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lus-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | bd5aa805782042d00876aad49dc71ce3 |
apache-2.0 | ['automatic-speech-recognition', 'google/fleurs', 'generated_from_trainer'] | false | facebook/wav2vec2-xls-r-1b This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the GOOGLE/FLEURS - PS_AF dataset. It achieves the following results on the evaluation set: - Loss: 4.1921 - Wer: 0.9295 - Cer: 0.9608 | a0c756d66f4292901cd34ee455e2833d |
apache-2.0 | ['automatic-speech-recognition', 'google/fleurs', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 3b3a4d716024af5078c724d0df0c8d9b |
apache-2.0 | ['automatic-speech-recognition', 'google/fleurs', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:----:|:------:|:---------------:|:------:| | 19.9558 | 1.27 | 100 | 3.2660 | 20.9197 | 1.0 | | 19.7186 | 2.53 | 200 | 1.1692 | 19.2447 | 1.0 | | 15.203 | 3.8 |... | 2a27d44b0a0b1821bcb556d1442512ba |
cc | [] | false | Training data [Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of Aug20, 2021 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) is used for both tokenizer and GPT-2 model. We splitted the dataset into three subsets - tra... | 122b6c82d7b7703eb0c33b572e6f7422 |
cc | [] | false | Model description The model architecture is the same as GPT-2 small model (n_ctx: 1024, n_embd 768, n_head: 12, n_layer: 12) except for a vocabulary size. The vocabulary size is set to 32,000 instead of an original size of 50,257. `transformers.GPT2LMHeadModel` is used for training. | e541dc1670faf1820fcc3d0592ede418 |
cc | [] | false | Tokenizer description [SentencePiece](https://github.com/google/sentencepiece) is used as a tokenizer for this model. We utilized 1,000,000 sentences from train set. The vocabulary size was 32,000. A `add_dummy_prefix` option was set to `True` because Japanese words are not separated by whitespaces. After training,... | 554ff29cd816effaca57e1765cc437aa |
cc | [] | false | Training The model was trained on the train set for 30 epochs with batch size 32. Each sample contained 1024 tokens. We utilized Adam optimizer. Learning rate was linearly increased from `0` to `1e-4` during the first 10,000 steps. A clip norm was set to `1.0`. Test set perplexity of the trained model was 29.13. P... | 43a257ee634c5f74f5a1a67ff91f39bd |
cc | [] | false | Usage First, install dependecies. ```sh $ pip install transformers==4.10.0 torch==1.8.1 sentencepiece==0.1.96 ``` Then use pipeline to generate sentences. ```sh >>> import transformers >>> pipeline = transformers.pipeline("text-generation", "colorfulscoop/gpt2-small-ja") >>> pipeline("統計的機械学習でのニューラルネットワーク", do_sam... | 8a9d41a81434fdae1ba67658eb6a3dc7 |
cc | [] | false | Versions We recommend to specify `revision` to load the model for reproducibility. | Revision | Date of Wikipedia dump | | --- | --- | | 20210820.1.0 | Aug 20, 2021 | | 20210301.1.0 | March 1, 2021 | You can specify `revision` as follows. ```py | abb1aad7d3d3ce712442650531f5b661 |
cc | [] | false | License All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). **Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use ... | cd194e224b0ce12115e0629f11c14e53 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech-sat_s423 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | a6e2ddf463816632ff847bcbd74db75a |
gpl-3.0 | ['object detection', 'computer vision', 'machine learning', 'yolo', 'yolov8'] | false | Yolov8 Inference ```python from ultralytics import YOLO model = YOLO('techzizou/yolov8x') model.conf = conf_threshold model.iou = iou_threshold prediction = model.predict(image, imgsz=image_size, show=False, save=False) ``` | 81fe4b294c0ee0ebc70954a392748ae2 |
mit | ['generated_from_trainer'] | false | final_bart_prepro_fix This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6100 - Rouge1: 35.5593 - Rouge2: 13.0497 - Rougel: 23.5672 - Bleu1: 29.5206 - Bleu2: 17.3914 - Ble... | 6550362731dae29ba13635e06899c0ee |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 980ce9cab7b178907bacc1facdf506d0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Rdass | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:| | 2.1622 | 1.51 ... | e591fa28723645247e7f1c6b405af093 |
apache-2.0 | ['translation'] | false | spa-afr * source group: Spanish * target group: Afrikaans * OPUS readme: [spa-afr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-afr/README.md) * model: transformer-align * source language(s): spa * target language(s): afr * model: transformer-align * pre-processing: normalization + Se... | 9e17b3fc18fe7f1f68e1f74025754365 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-afr - source_languages: spa - target_languages: afr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-afr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'af'] - src_constituents: {'spa'} - tgt_const... | 2c1cfaa7b93fa7a85c7df1f74681792c |
apache-2.0 | ['summarization', 'token-classification', 't5'] | false | How to use Colab: [link](https://colab.research.google.com/drive/1Q8_v3H-kxdJhZIiyLYat7Kj02qDq7M1L) ```python import razdel from transformers import AutoTokenizer, BertForTokenClassification model_name = "IlyaGusev/rubert_ext_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) sep_token = tokenizer.s... | e2430c361703db7e65d4df18b4c51c87 |
apache-2.0 | ['summarization', 'token-classification', 't5'] | false | Fix token_type_ids current_token_type_id = 0 for pos, input_id in enumerate(inputs["input_ids"][0]): inputs["token_type_ids"][0][pos] = current_token_type_id if input_id == sep_token_id: current_token_type_id = 1 - current_token_type_id | 26eca8658ba0d31b16a0e382bf9dd383 |
apache-2.0 | ['summarization', 'token-classification', 't5'] | false | Choose sentences logits = logits[sep_mask] logits, indices = logits.sort(descending=True) logits, indices = logits.cpu().tolist(), indices.cpu().tolist() pairs = list(zip(logits, indices)) pairs = pairs[:3] indices = list(sorted([idx for _, idx in pairs])) summary = " ".join([sentences[idx] for idx in indices]) print... | 1954c7f6625391f2def8e581a9c615b2 |
apache-2.0 | ['generated_from_trainer'] | false | emotion_trained_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9045 - F1: 0.7328 | 6ab0ece76bba0082941f131ed0c4df3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6480 | 0.7231 | | No log | 2.0 | 408 | 0.6114 | 0.7403 | | 0.5045 | 3.0 | 612 | 0.7593 | 0.7311 | |... | 0bc49eb73506178fe18382a0a4a1b0d4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-sanitycheck This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0092 - Accuracy: 1.0 | fd14a8a392e9e2f7952dde64783a72cf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | b86891a582c4ce8eff0de2f63af73cb8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.14 | 8 | 0.8034 | 0.4737 | | No log | 2.29 | 16 | 0.6803 | 0.5263 | | No log | 3.43 | 24 | 0.4867 | 1.... | 0190b6056f5c4a598149a58298d85a54 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 1700k (uncased) Seed 0 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | 8e1773adc911e07c63e60c9a712a3af7 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1700k') model = BertModel.from_pretrained("multiberts-seed-0-1700k") text = "Replace me by any text you'd lik... | a3224e14ab608e5db8078595f3ff6e1c |
apache-2.0 | ['translation'] | false | vie-spa * source group: Vietnamese * target group: Spanish * OPUS readme: [vie-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-spa/README.md) * model: transformer-align * source language(s): vie * target language(s): spa * model: transformer-align * pre-processing: normalization + S... | 62bf9728aedbe9a94e0187554d900175 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: vie-spa - source_languages: vie - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'es'] - src_constituents: {'vie', 'vie_Hani'} ... | 9789643a082354ab7ae405c42dd957f7 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'Libri3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | SepFormer trained on Libri3Mix This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on Libri3Mix dataset. For a better experience we encourage you to learn more about [SpeechBrain... | 1aedf3be031a15ef31090b0d44023d52 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'Libri3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | Perform source separation on your own audio file ```python from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-libri3mix", savedir='pretrained_models/sepformer-libri3mix') est_sources = model.separate_file(path='speechbra... | d41e9b4f1066c1dbd4ce33acc5435126 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'Libri3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | Training The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows 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/Libr... | 46e1bc7895ea17d618232396c742ee4a |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'Libri3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | Referencing SepFormer ```bibtex @inproceedings{subakan2021attention, title={Attention is All You Need in Speech Separation}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, year={2021}, booktitle={ICASSP 2021} } @misc{subakan2022sepformer au... | 9cfc77b14ce4a7f35476931a9635d4c7 |
mit | [] | false | Joe Whiteford Art Style on Stable Diffusion This is the `<joe-whiteford-artstyle>` 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.ipyn... | 74aee02ae0f5f41f5f9c8e6430aa8077 |
mit | ['nlp', 'roberta', 'xlmr', 'classifier', 'aer', 'narrative', 'entity recognition'] | false | An XLM-Roberta based language model fine-tuned for AER (Actionable Entities Recognition) -- recognition of entities that protagonists could interact with for further plot development. We used 5K+ locations from 1K interactive text fiction games and extracted textual descriptions of locations and lists of actionable e... | 6c9a9c427773fe2e180f473298af0ad9 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sa-twitter This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4741 - Accuracy: 0.7925 - F1: 0.7949 | e44c219b9c8a84894724b18faacfa25e |
mit | ['generated_from_trainer'] | false | bart-large-cnn-weaksup-original-100k This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5931 - Rouge1: 30.4429 - Rouge2: 15.6691 - Rougel: 24.1975 - Rougelsum: 27.4761... | cea88cd5e9e8b01375fa0b7dfdc2ceaf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.261 | 1.0 | 100000 | 1.5931 | 30.4429 | 15.6691 | 24.1975 | 27.4761 ... | eea4df8b7bebd646f36cf9a0329d6853 |
mit | ['generated_from_trainer'] | false | twitter-data-xlm-roberta-base-hindi-only-memes 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: 0.4006 - Accuracy: 0.9240 - Precision: 0.9255 - Recall: 0.9263 - F1: 0.9259 | 0fd16cd79f9ff6dec07fc9aa70a9e733 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 6 | a5dde2632318e162987c438451b4546f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.7485 | 1.0 | 511 | 0.4062 | 0.8381 | 0.8520 | 0.8422 | 0.8417 | | 0.4253 | 2.0 |... | c3bd6e87b5b9821559a862c1b9c015ee |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-french Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in French using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | e2df077962cbfba310ee2d82d329a05d |
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", "fr", split="test[:20%]") processor = Wav2Vec2Processor.from_... | bed6ceb7d1b1b8ba7761217f95729a65 |
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["speech"][... | 33b22b7344ab97df6d990ee67556ec87 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the French 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", "fr") wer = load_metric("... | 5af5650eb49919654c60970bec1c0611 |
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_dataset = te... | b074d895a605aec8495df4d9dd7beafe |
apache-2.0 | ['generated_from_keras_callback'] | false | silviacamplani/distilbert-uncase-direct-finetuning-ai-ner_3labels 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: 0.6593 - Validation Loss: 0.6130 - Epoch: 9 | 0bfa87268c887b5d7e96ad625e27bdef |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 60, 'end_learning_rate... | dce7be2d4785a05d991adf3959dc90b7 |
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