license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | stbl_clinical_bert_ft_rs6 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0876 - F1: 0.9177 | a08a302047f970bd496e99d65c252b5b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2778 | 1.0 | 101 | 0.0871 | 0.8482 | | 0.066 | 2.0 | 202 | 0.0700 | 0.8892 | | 0.031 | 3.0 | 303 | 0.0657 | 0.9053 | |... | 9404991ce10b2e9670627d2a8943257d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-hindi-bhoj-3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.7033 - Wer: 1.1477 | ceed3973fa5b8e0825f9176e91e5c098 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.6136 | 6.45 | 400 | 3.6017 | 1.0 | | 2.6692 | 12.9 | 800 | 4.5408 | 1.0872 | | 0.5639 | 19.35 | 1200 | 5.2302 | 1.2282 | |... | 47f3ccddc47914612638a681c2711efb |
mit | [] | false | model by Skittleology This your the Stable Diffusion model fine-tuned the Pikachu concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **pikachu** Model requested by Pikachu, an Uberduck admin/user. You can also train your own concepts and upload them to the library ... | 9eb1d196229d74bdb0b5b683f1ab6ceb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 53a52190b419474189ca42d54e15253e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-fine-tuned-blbooksgenre This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the blbooksgenre dataset. It achieves the following results on the evaluation set: - Loss: 1.9631 | fe4ff2657acb986aee10d2fffb3e2f25 |
apache-2.0 | ['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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP | 44e3cdf717349d745311d53ddc3ff7b2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2575 | 1.0 | 6226 | 2.1388 | | 2.0548 | 2.0 | 12452 | 2.0312 | | 1.988 | 3.0 | 18678 | 1.9631 | | d64bf90ccb5657a9af2de15d803a3873 |
apache-2.0 | ['generated_from_trainer'] | false | IMDB_ELECTRA_5E This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2158 - Accuracy: 0.9533 | 764e22ac179abfdd2a8425726ffed379 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6784 | 0.03 | 50 | 0.6027 | 0.84 | | 0.4378 | 0.06 | 100 | 0.2217 | 0.9533 | | 0.3063 | 0.1 | 150 | 0.1879 | 0.... | 0cb361761d70a1aba9e8c4adcec9d238 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Precision: 0.9293 - Recall: 0.9385 - F1: 0.9339 - Accuracy: 0.9843 | 79b438921e83dc85da49c0d6ef3e0692 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2436 | 1.0 | 878 | 0.0670 | 0.9190 | 0.9240 | 0.9215 | 0.9815 | | 0.0505 | 2.0 |... | e071563293d546c7a32252174af334fd |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | wav2vec2-xls-r-1b-ca This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/p... | 2b07c4f98f1fda942134dd2d45535e8f |
mit | ['sentiment-analysis'] | false | Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_sa_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_sa_v7_distil") ``` | b68ae8838af4e56f0c194a7862b59f17 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper_batch_16_e8 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 sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3951 - Accuracy: 0.9129 | 59893b3255604432c7cf14c7384e6161 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 - num_epochs: 8 - mixed_precision_training: Apex, opt level O1 | a26e983aa2c2b1e6c0e084e4a8d83785 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8115 | 0.16 | 100 | 3.7948 | 0.1862 | | 3.1194 | 0.31 | 200 | 3.0120 | 0.3281 | | 2.3703 | 0.47 | 300 | 2.4791 | 0.... | 92724c4d5e9466228a4f95f98f6bd094 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch', 'text classificatio'] | false | Guwen CLS A Classical Chinese Text Classifier. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owner=... | 639bcb1fb4224de385671b86fbda3ba7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 158 | 2.1038 | | No log | 2.0 | 316 | 2.0349 | | 8c6602ac97bdb122a6c46680ca1a1d93 |
afl-3.0 | [] | false | Inference ```python from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline tokenizer = AutoTokenizer.from_pretrained("SRDdev/SRDBerta") model = AutoModelForMaskedLM.from_pretrained("SRDdev/SRDBerta") fill = pipeline('fill-mask', model='SRDberta', tokenizer='SRDberta') ``` ```python fill_mask = fill... | c4ad1e613c03118bf0af5656792a84d3 |
afl-3.0 | [] | false | Citation Author: @[SRDdev](https://huggingface.co/SRDdev) ``` Name : Shreyas Dixit framework : Pytorch Year: Jan 2023 Pipeline : fill-mask Github : https://github.com/SRDdev LinkedIn : https://www.linkedin.com/in/srddev/ ``` | 75aa56a645cad25c3dc607f3c61e28e8 |
apache-2.0 | ['generated_from_keras_callback'] | false | jo0hnd0e/distilbert-finetuned-imdb 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: 2.8526 - Validation Loss: 2.6015 - Epoch: 0 | f05587058f2cdefda1903443d5695520 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | fab6bb905018814d21f37b238eae8924 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE (Deep-Narrow version) T5-Efficient-LARGE is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was re... | 1a50ed6c82a3bdec4012fc8e6c5a64e7 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large** - is of model type **Large** with no variations. It has **737.72** million parameters and thus requires *ca.* **2950.9 MB** of memory in full precision (*fp32*) or **1475.45 MB** of memory in half precision (*fp16* or *bf16*). A summary of t... | 74bf1beb1fd56c19b186aaa17178244d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Javanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [OpenSLR High quality TTS data for Javanese](https://openslr.org/41/). When using this model, make sure that your speech input is sampled at 16kHz. | 8fc4d5011a36a7e6a66c15b031f83abc |
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, load_metric, Dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets.utils.download_manager import DownloadManager from pathlib import Path im... | 804803888ca19785bb53108e61d66b87 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_javanese() test_dataset = dataset['test'] processor = Wav2Vec2Processor.... | 2f27f6a20b59a717bb18f2c340cd4daf |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows or using this [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb) ```python import torch import torchaudio from datasets import load_dataset, load_metric, Dataset from transformers... | 71b5dac7baba45201101702316f12d33 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_javanese() test_dataset = dataset['test'] wer = load_metric("wer") processo... | 9dca50864681d5c9ae0279eca1986fd2 |
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... | 270026d9c3bd089f353b6653d3a7f45a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training [OpenSLR High quality TTS data for Javanese](https://openslr.org/41/) was used for training. The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb) and to [evaluate it](https://github.c... | 8761edc33d3f2e3c3ccc309e472a54c7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-960h-lv60-self-intent-classification-ori This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1985 - Accuracy: 0.5417 | d9033664c93a58f57fceb958310d8de6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | dd89428c8c1a8c9b6c751507a06c8e7f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2033 | 1.0 | 14 | 2.2126 | 0.0833 | | 2.2006 | 2.0 | 28 | 2.2026 | 0.0833 | | 2.1786 | 3.0 | 42 | 2.1758 | 0.... | 716c3e97b9af95f14af6235532fbaae7 |
apache-2.0 | ['vision', 'object-detection'] | false | Model Details The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh ... | 403e6b02547ed5351a2f9eba9e8e4146 |
apache-2.0 | ['vision', 'object-detection'] | false | Model Type The model uses a CLIP backbone with a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine... | 142883443b1cdbf1dea59c268cdba1b5 |
apache-2.0 | ['vision', 'object-detection'] | false | Use with Transformers ```python3 import requests from PIL import Image import torch from transformers import OwlViTProcessor, OwlViTForObjectDetection processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32") model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32") url = "http... | 1c263e15964bf260025f31bedfd40947 |
apache-2.0 | ['vision', 'object-detection'] | false | Print detected objects and rescaled box coordinates score_threshold = 0.1 for box, score, label in zip(boxes, scores, labels): box = [round(i, 2) for i in box.tolist()] if score >= score_threshold: print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") ``` | 9afff642135f9ff64e17185025ec6102 |
apache-2.0 | ['vision', 'object-detection'] | false | Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially ... | 980845773ae3f9f4a16c55f731c3c7da |
apache-2.0 | ['vision', 'object-detection'] | false | Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. | 6251d7d0b7081340c1157d7ff5433b85 |
apache-2.0 | ['vision', 'object-detection'] | false | Data The CLIP backbone of the model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our c... | 4b8532ea7715dd2b0b7b69eb5cdf01ae |
apache-2.0 | ['vision', 'object-detection'] | false | BibTeX entry and citation info ```bibtex @article{minderer2022simple, title={Simple Open-Vocabulary Object Detection with Vision Transformers}, author={Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuora... | 548e1e07fe73df8a545bc53a1e9820c6 |
apache-2.0 | ['generated_from_trainer'] | false | youtube-bert_10 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: 2.4456 - Perplexity: 11.54 | b30191bde90df8c2f8d75ab7fb1127fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6799 | 1.0 | 1899 | 2.5135 | | 2.5736 | 2.0 | 3798 | 2.4612 | | 2.5172 | 3.0 | 5697 | 2.4363 | | 1793683bbff544f9c23012e985202bd2 |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/commonvoice/asr1 ./asr.sh \ --stage 1 \ --stop_stage 13 \ --nj 32 \ --inference_nj 32 \ --skip_train true \ --train_set "train_zh_TW" \ --valid_set "dev_zh_TW" \ --test_sets "dev_zh_TW test_zh_TW" \ --lang "zh_TW" \ --local_data... | 75d2bd55ae08ff2d7c220b4723c384ec |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | Environments - date: `Thu Sep 1 21:49:10 UTC 2022` - python version: `3.9.12 (main, Jun 1 2022, 11:38:51) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.12.1+cu102` - Git hash: `13db69d3befc3c82a5ff5a11e28bf79d5030603f` - Commit date: `Mon Aug 29 13:44:35 2022 +0000` | 600343f79a7e8928758d8efeabbe8001 |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.best/dev_zh_TW|2627|22200|97.7|2.1|0.2|0.0|2.4|9.5| |inference_asr_model_valid.acc.best/test_zh_TW|2627|21991|98.0|1.6|0.4|0.1|2.1|7.7| | 2c2b4788ca424c968865ce742865d9aa |
cc-by-4.0 | ['espnet', 'audio', 'speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.best/dev_zh_TW|2627|24827|98.6|1.2|0.2|0.0|1.5|4.0| |inference_asr_model_valid.acc.best/test_zh_TW|2627|24618|98.8|0.9|0.4|0.1|1.3|3.4| | 4e66d1c28194de79d9f354b629949310 |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | bert-base-uncased-yelp_polarity This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yelp_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.3222 - Accuracy: 0.9516 | e9dba7e22bb1232cc311d2f6154e60fb |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - 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: 277200 - training_steps: 2772000 | 98b09105ba8fff87ed9a75c717cf0769 |
apache-2.0 | ['generated_from_trainer', 'sibyl'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8067 | 0.0 | 2000 | 0.8241 | 0.4975 | | 0.5482 | 0.01 | 4000 | 0.3507 | 0.8591 | | 0.3427 | 0.01 | 6000 | 0.3750 ... | c81b451e0bef8831b46633a38c6af8f4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1539 | a05db8939bb3142799e8d3bc0e9a81a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2221 | 1.0 | 5533 | 1.1611 | | 0.9677 | 2.0 | 11066 | 1.1226 | | 0.7567 | 3.0 | 16599 | 1.1539 | | 0897ceb1c30d99b5c518e99ab2dc1b2e |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squadshifts-amazon-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com... | 3ff0da1c4f65cde6e3eb7fbd38adcd45 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (amazon) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://gith... | 595938d598df1a13ead1b66d58ac7fbf |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-squadshifts-amazon-... | f2b5ea0daaba1917625e1738d9a9ecde |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | Score | Type | Dataset ... | b4c3b649e9e2e3abcc55f1c82a37a72d |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: amazon - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoc... | 1eda10bb7f88f0dec72e7d71d323c0fa |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_102_qnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5033 - Accuracy: 0.9043 | 5ffb0de2bc3f3b114205d84f4e9c9262 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard'] | false | Training Results | Training Loss | Epoch | Step | WER | |:-------------:|:-----:|:----:|:----:| | 0.1111 | 0.39 | 1000 | 9.89 | | 0.0884 | 0.78 | 2000 | 9.26 | | 0.0362 | 1.17 | 3000 | 8.64 | | 0.0359 | 1.56 | 4000 | 8.60 | | 0.0375 | 1.95 | 5000 | 8.24 | : : | 0.0015 |... | 2f9e4d4c6eaebad4ccf8150734c01167 |
mit | ['gpt2-indo-medium-kids-stories'] | false | GPT-2 Indonesian Medium Kids Stories GPT-2 Indonesian Medium Kids Stories is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. The model was originally the pre-trained [GPT2 Medium Indonesian](https://huggi... | 3af940a5acca41c04646d63a6301bcb1 |
mit | ['gpt2-indo-medium-kids-stories'] | false | params | Arch. | Training/Validation data (text) | | ------------------------------- | ------- | ----------- | --------------------------------- | | `gpt2-indo-medium-kids-stories` | 345M | GPT2 Medium | Indonesian Kids' Stories (860 KB) | | 1ce77c454d7f97f6f903c70f6a1751ad |
mit | ['gpt2-indo-medium-kids-stories'] | false | Evaluation Results The model was fine-tuned for 3 epochs. | Epoch | Training Loss | Validation Loss | | ----- | ------------- | --------------- | | 1 | 3.909100 | 3.627678 | | 2 | 3.375300 | 3.562854 | | 3 | 3.113300 | 3.578999 | | e1373321f292312118e70179364e2b8e |
mit | ['gpt2-indo-medium-kids-stories'] | false | As Causal Language Model ```python from transformers import pipeline pretrained_name = "bookbot/gpt2-indo-medium-kids-stories" nlp = pipeline( "text-generation", model=pretrained_name, tokenizer=pretrained_name ) nlp("Archie sedang mengendarai roket ke planet Mars.") ``` | 5a188867204b35e07650099dc3d9da30 |
mit | ['gpt2-indo-medium-kids-stories'] | false | Feature Extraction in PyTorch ```python from transformers import GPT2LMHeadModel, GPT2TokenizerFast pretrained_name = "bookbot/gpt2-indo-medium-kids-stories" model = GPT2LMHeadModel.from_pretrained(pretrained_name) tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name) prompt = "Archie sedang mengendarai ro... | f0b485629286a2b5a9cd4afca4ffef01 |
mit | ['gpt2-indo-medium-kids-stories'] | false | Author GPT-2 Indonesian Medium Kids Stories was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. | fa72516b3353fa843dc308ebbfb93d56 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sah', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | sammy786/wav2vec2-xlsr-sakha This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - sah dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and ... | 4ceec4f9c08fbae3f440ecd5d01f0d5e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sah', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_ty... | ca18a399d1ad7ee98cfc6c3ef79c2d1f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sah', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 4.541600 | 1.044711 | 0.926395 | | 400 | 1.013700 | 0.290368 | 0.401758 | | 600 | 0.645000 | 0.232261 | 0.346555 | | 800 | 0.467800 | 0.214... | 204739e1a132876bbfc3e126925c1fc6 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sah', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-sakha --dataset mozilla-foundation/common_voice_8_0 --config sah --split test ``` | af83bbb38459f13b015482e429e7475c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1473 | 7ddd6400ee09554cff7abb17ca702e58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2114 | 1.0 | 5533 | 1.1509 | | 0.9547 | 2.0 | 11066 | 1.1188 | | 0.7544 | 3.0 | 16599 | 1.1473 | | 723bcb555ff77c3bde5534674b619669 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.7132 - Accuracy: 0.8603 - F1: 0.9026 - Combined Score: 0.8814 The model was fine-tuned to compare... | 0fbd5293333255d2110e5ccd92700824 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n... | c294b861779d86e15a5c4f1b54f81f2a |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5981 | 1.0 | 230 | 0.4580 | 0.7892 | 0.8562 | 0.8227 | | 0.3739 | 2.0 | 460 | 0.38... | ef2f7efc8f347a8e39c7711cb00340b3 |
apache-2.0 | ['generated_from_keras_callback'] | false | adtabora/distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8581 - Validation Loss: 3.6738 - Epoch: 0 | 30faa2a908133483ee34dadc547eee3c |
mit | [] | false | PastelArtStyle on Stable Diffusion This is the `<Arzy>` 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 t... | 412254cb8bfa1d991ca60329aa95539a |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | DistilT5 for question-generation This is distilled version of [t5-base-qg-hl](https://huggingface.co/valhalla/t5-base-qg-hl) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. The model is distilled using the **No Teacher Distillati... | f76a1c36632c08cacfdcf228a6161a52 |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | distilbart). We just copy alternating layers from `t5-base-qg-hl` and finetune more on the same data. Following table lists other distilled models and their metrics. | Name | BLEU-4 | METEOR | ROUGE-L | QA-EM | QA-F1 | |-------------------... | d58855f8208ff093d20c537b329c13c7 |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | Model in action 🚀 You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) ```python3 from pipelin... | 232fde470ca1882284365ebebbdc1960 |
mit | [] | false | Amine on Stable Diffusion This is the `<ayna>` 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 your... | d3cde989910f18bfdc71308e5c40c4dc |
openrail | ['stable-diffusion', 'embedding', 'textual inversion'] | false | Dreamink <img src="https://huggingface.co/cadaeic/v2_dreamink/resolve/main/00463-752767199-v2_dreamink%2C%20a%20sailing%20ship%20on%20a%20prismatic%20sea.png" width="300"/> A style embedding for Stable Diffusion v2 (768) of striking stark silhouetted landscapes against colourful backgrounds. Not compatible with SD v... | 6930aca419843bad84950741e5563be5 |
openrail | ['stable-diffusion', 'embedding', 'textual inversion'] | false | Prompts Above images settings:\ **Prompt 1**: v2_dreamink, a sailing ship on a prismatic sea\ **Prompt 2**: v2_dreamink, a cozy library full of bookshelves\ **Steps**: 15, **Sampler**: DPM adaptive, **CFG scale**: 7, **Seed**: 752767199, **Size**: 768x768, **Model**: Stable Diffusion 2.1 (768) <img src="https://hugg... | 5e18ac0ca34cee641909b1f42fd58c10 |
openrail | ['stable-diffusion', 'embedding', 'textual inversion'] | false | Suggestions - The sharp lines of the DPM++ samplers work well with Dreamink, and I particularly suggest trying DPM Adaptive out. - Works best with landscapes, haven't really tried this out with characters and portraits and I think it might struggle with those. - Definitely slightly overtrained on the sci fi influence... | 620227c0bd7746502650de9fb7dc1298 |
openrail | ['stable-diffusion', 'embedding', 'textual inversion'] | false | Training Trained and generated in Automatic1111's Webui Images generated from a model merge of Inkpunk Diffusion and Dreamlike Diffusion at 0.3, then mostly generated with the following template:\ **Prompt**: Subject matter, (nvinkpunk:0.8), (dreamlikeart:0.8), cel shaded, flat, synthwave, chiaroscuro, by Winslow Ho... | 4f12704631efd2f2d5a0e6209cb75a90 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod_4 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_3](https://huggingface.co/Hoax0930/kyoto_marian_mod_3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8237 - Bleu: 21.5586 | a50b9a42f068b7946fe900ccfdaedf81 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_wavlm_s132 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sample... | b434bcfe5ecfd80f39b32a30d333a7f8 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT_fold_4_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7349 - F1: 0.8052 | be847e51eccb047de1726bd35b8aec54 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5378 | 0.7818 | | 0.5561 | 2.0 | 578 | 0.4835 | 0.8002 | | 0.5561 | 3.0 | 867 | 0.6401 | 0.7978 | |... | 8a73e105827718e282570e1d65dae9b6 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ggaabboommeerr Dreambooth model trained by gababas with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | c7f1ae8cedfe525ca512877c8a3ba985 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_text_headline_padded 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: 2.0168 - Validation Loss: 1.9047 - Epoch: 5 | a12ddacd77b298ce7bad218c93bd4a4f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1774 | 1.9726 | 0 | | 2.1349 | 1.9598 | 1 | | 2.1275 | 1.9362 | 2 | | 2.0746 | 1.9965 | 3 | | 2.0493 | 1.9394 | 4 | | 2.0168 |... | 583d2e9cf007324e38b1dcfd15981292 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech-sat_s775 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | ab76ad7d11387dcf12d890f539982339 |
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 3b54bfe52a294cdfce668c20d777bfa65f413745 pip install -e . cd egs2/fsc_challenge/slu1 ./run.sh --skip_data_prep false --skip_tra... | 0ad119df6ed9af70b120c902577b504d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Mar 13 20:59:06 EDT 2022` - python version: `3.8.11 (default, Aug 3 2021, 15:09:35) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a3` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `97b9dad4dbca71702cb7928a126ec45d96414a3f` - Commit date: `Mon Sep 13 22:55:04 2021 +0900` | e852103993eb8c47d4ca69a910bb41ae |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/spk_test|3349|17937|99.9|0.1|0.0|0.0|0.1|0.6| |inference_asr_model_valid.acc.ave_5best/utt_test|4204|22540|89.8|6.6|3.6|0.0|10.2|27.6| | 5c6986fb632edd10064bdfc9b4ddb9d5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/spk_test|3349|152191|100.0|0.0|0.0|0.0|0.1|0.6| |inference_asr_model_valid.acc.ave_5best/utt_test|4204|191435|94.5|2.8|2.7|0.5|6.0|27.6| | 269efc24a2096132857b46935ff275a5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_hubert_transformer_adam_specaug_deliberation_transformer_3.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_hubert_transformer_adam_specaug_deliberation_transformer_3_raw_en_wo... | 508c47b3f7038ee14c127b7630b5a37e |
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