license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_esquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 256 - ... | 8250b5067f5ce07c484b2ce462e3546f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-spanish-custom 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: 0.4426 - Wer: 0.2117 | 0dad8b7029a5e2d65626e63a7b8e63ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.2307 | 0.4 | 400 | 1.4431 | 0.9299 | | 0.7066 | 0.79 | 800 | 0.5928 | 0.4836 | | 0.4397 | 1.19 | 1200 | 0.4341 | 0.373... | a3f3797fb3c2a6aac67c73ff3ec8ed49 |
mit | ['generated_from_trainer'] | false | xlmr_mask_punctuation This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5160 | ba3f92ed2eaeacd9835329d390902891 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6352 | 0.05 | 500 | 1.4744 | | 1.4623 | 0.11 | 1000 | 1.0987 | | 1.1947 | 0.16 | 1500 | 1.1878 | | 1.0693 | 0.21 | 2000 | 0.8077 ... | 1bd3c7c72088824c50b35c4eebdd12b5 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ceb * source languages: fi * target languages: ceb * OPUS readme: [fi-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ceb/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 2bc7bc269f996957cd17652825b93006 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8556 - Bleu: 52.9897 | 30a6f50d54a3557a896748b6140579e4 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mpid-hassanblend-v1-4-last-version Dreambooth model trained by tftgregrge 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/githu... | 89b6cae3cf7655627ea57487bf8c8807 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-wiki_shake_mask 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: 2.4464 | 9d525fb2cc4fa5dd02da636c07855a00 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6528 | 1.0 | 3015 | 2.5390 | | 2.5536 | 2.0 | 6030 | 2.4558 | | 2.5396 | 3.0 | 9045 | 2.4464 | | 071f3954fea3d6619c3fbc644902f8b7 |
mit | ['generated_from_trainer'] | false | deberta-base-nepali This model is pre-trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset consisting of over 13 million Nepali text sequences using a masked language modeling (MLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization ... | a0fc4296c9f9162dc8b5c9ad98ffa5ca |
mit | ['generated_from_trainer'] | false | Intended uses & limitations This backbone model intends to be fine-tuned on Nepali language focused downstream task such as sequence classification, token classification or question answering. The language model being trained on a data with texts grouped to a block size of 512, it handles text sequence up to 512 tok... | 24a3439da826858947159860940059b0 |
mit | ['generated_from_trainer'] | false | Usage This model can be used directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Sakonii/deberta-base-nepali') >>> unmasker("मानविय गतिविधिले प्रातृतिक पर्यावरन प्रनालीलाई अपरिमेय क्षति पु्र्याएको छ। परिवर्तनशिल जलवायुले ख... | 59b372a1c7875d59367fce1327c299f6 |
mit | ['generated_from_trainer'] | false | Training data This model is trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) language modeling dataset which combines the datasets: [OSCAR](https://huggingface.co/datasets/oscar) , [cc100](https://huggingface.co/datasets/cc100) and a set of scraped Nepali articles on ... | c0caed2f45e850bccc621af95598a903 |
mit | ['generated_from_trainer'] | false | Tokenization A Sentence Piece Model (SPM) is trained on a subset of [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset for text tokenization. The tokenizer trained with vocab-size=24576, min-frequency=4, limit-alphabet=1000 and model-max-length=512. | e5361ec4a362be80d75fe733b63fe244 |
mit | ['generated_from_trainer'] | false | Training procedure The model is trained with the same configuration as the original [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base); 512 tokens per instance, 6 instances per batch, and around 188.8K training steps (per epoch). | 5e73ab3320c80dd0292d42e97cef45b2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 7a57cd5aeb4603fc572462608c41a51b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Perplexity | |:-------------:|:-----:|:------:|:---------------:|:----------:| | 2.5454 | 1.0 | 188789 | 2.4273 | 11.3283 | | 2.2592 | 2.0 | 377578 | 2.1448 | 8.5403 | | 2.1171 | 3.0 | 566367 | 2.... | 8b5ab12b336fa22809008f3112bfc657 |
apache-2.0 | ['generated_from_trainer'] | false | all-15-bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0081 - Precision: 0.9630 - Recall: 0.9661 - F1: 0.9646 - Accuracy: 0.9987 | 906d0edbfa46579f3b31caa5dd3bf03c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.014 | 1.0 | 6693 | 0.0080 | 0.9048 | 0.9363 | 0.9203 | 0.9976 | | 0.007 | 2... | cfbc615d130adca0588976e8ce5f17ab |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper_batch_32_e4 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: 0.3909 - Accuracy: 0.9067 | 8f8690349b03a122347de70ba1cbc04a |
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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Apex, opt level O1 | 91565f2fa8af72edd0f4f446f80f92d5 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.4295 | 0.31 | 100 | 3.4027 | 0.2837 | | 2.5035 | 0.62 | 200 | 2.4339 | 0.5247 | | 1.6542 | 0.94 | 300 | 1.7690 | 0.... | 70584660a938a25ccd964761bd7430e1 |
mit | ['generated_from_trainer'] | false | my_deneme_3_epoch This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2653 - Precision: 0.8985 - Recall: 0.8916 - F1: 0.8950 - Accuracy: 0.9259 | b6498c2d1f71fbc38fbc4cff1628c42a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 488 | 0.3634 | 0.8684 | 0.8580 | 0.8632 | 0.9029 | | 0.6954 | 2.0 |... | 5ccd933b3da5f15bc901404e3c965b12 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_100v6_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v6_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4381 - Precision: 0.2402 - Recall: 0.1964 - F1: 0.2161 - Accura... | 33262669cd88d35dcdac75bba2d98582 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 46 | 0.4630 | 0.1977 | 0.1254 | 0.1534 | 0.8317 | | No log | 2.0 |... | 7a3b77d07f93401a35f9de2cbfdcea17 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'lt', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | sammy786/wav2vec2-xlsr-lithuanian 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 - lt dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other ... | 34af0e264dc409dc87d616d6bf999644 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'lt', '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: 8 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_typ... | a99f2758509006fbacb16b9545b9126e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'lt', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Step | Training Loss | Validation Loss | Wer | |:-----:|:-------------:|:---------------:|:--------:| | 200 | 5.718700 | 2.897032 | 1.000000 | | 400 | 1.340000 | 0.309548 | 0.507284 | | 600 | 0.799100 | 0.220205 | 0.402098 | | 800 | 0.494400 |... | d8ec1b2076512c7a32ab67072d68c71b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'lt', '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-lithuanian --dataset mozilla-foundation/common_voice_8_0 --config lt --split test ``` | 6d59a0f4c9e51b147a84ff314db9a616 |
mit | ['generated_from_trainer'] | false | robbert-dutch-base-squad-nl This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5735 | eed8d84d15c95348e55ee8647a1cf0f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8337 | 1.0 | 4162 | 1.5621 | | 1.5251 | 2.0 | 8324 | 1.5735 | | 6da6b918047c79ae46e6a354c2488ba1 |
mit | [] | false | Ouroboros on Stable Diffusion This is the `<ouroboros>` 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... | c3c7a73b9b4ba624fbde51a399581497 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Hebrew This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs he_il dataset. It achieves the following results on the evaluation set: - Wer: 34 | 175a1e7c45849e629b372f42aa304def |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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_precis... | 2c0be8b7e3a2f1dead210073d2d0d883 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2525 - Accuracy: 0.9468 | 5fd80fb5274acdecd3e3352d53db49da |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | e0a40665a6a24f583fe885b4349095e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2246 | 1.0 | 318 | 3.1584 | 0.7545 | | 2.4033 | 2.0 | 636 | 1.5656 | 0.8652 | | 1.1684 | 3.0 | 954 | 0.7795 | 0.... | 104af7a35b33850e44c33610ab6b677e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Fr - Joss This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 FR dataset. It achieves the following results on the evaluation set: - Loss: 0.4212 - Wer: 24.0365 | a51cc688ccfdfc0ceb13ec8170271c1c |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3803 | 0.99 | 1000 | 0.3992 | 23.9465 | | 0.2214 | 1.99 | 2000 | 0.3902 | 22.8108 | | 0.0986 | 2.98 | 3000 | 0.4028 | 22.445... | db61f47ffebe73e5fb6b7e3eeefb497e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | nicolepamaral_v1 Dreambooth model trained by JP2004 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-stab... | 4c766c527fb73bd96638743845422359 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Introduction With the current wave of Artificial Intelligence and Digital Humanities sweeping the world, the automatic analysis of modern Chinese has achieved great results. However, the automatic analysis and research of ancient Chinese is relatively weak, and it is difficult to meet the actual needs of Sinology, hi... | e2e2dd93fb64f014fc3d95fc3c1f5c52 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Further Pre-training **Compared with the previous pre-trained models, `bert-ancient-chinese` mainly has the following characteristics:** - Ancient Chinese texts mostly appear in traditional Chinese characters and contain a large number of uncommon Chinese characters, which makes the `vocab table` (vocabulary) of the... | b107460095593a73ca776cf7b331aa31 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain `bert-ancient-chinese` model online. ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jihuai/bert-ancient-c... | 499b9dde9acaaae462075c5ef36cf8ce |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | From Huggingface Download directly through Huggingface's official website, and the model on the official website has been updated to the latest version simultaneously: - **bert-ancient-chinese:[Jihuai/bert-ancient-chinese · Hugging Face](https://huggingface.co/Jihuai/bert-ancient-chinese)** | 101e01dbcc2678e96af1e5cd809c6288 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | From Cloud Disk Download address: | Model | Link | | :------------------: | :----------------------------------------------------------: | | bert-ancient-chinese | [Link](https://pan.baidu.com/s/1JC5_64gLT07wgG2hjzqxjg ) Extraction code: qs7x | ... | ddf06027820bb8e9567a8c4cba140dbf |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Evaluation & Results We tested and compared different pre-trained models on the training and test sets provided by the competition [EvaHan 2022](https://circse.github.io/LT4HALA/2022/EvaHan). We compare the performance of the models by fine-tuning them on the downstream tasks of `Chinese Word Segmentation(CWS)` and `... | 506be44cdde85d96af4c220745297003 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Disclaim The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. **Users can use the model arbitrarily wi... | 7c6e7710ff7f35bf0c429bcb938ab66c |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Acknowledgment `bert-ancient-chinese` is based on [bert-base-chinese](https://huggingface.co/bert-base-chinese) to continue training. Thanks to Prof. [Xipeng Qiu](https://xpqiu.github.io/) and the [Natural Language Processing Laboratory of Fudan University](https://nlp.fudan.edu.cn/). | 1f6dfa9c4165777bbb9ab655e7255de0 |
mit | ['generated_from_trainer'] | false | codeparrot-ds-sample This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.5219 - eval_runtime: 603.3856 - eval_samples_per_second: 154.402 - eval_steps_per_second: 4.826 - epoch: 0.15 - step: 10000 | 22ccc0ad13cdfe3361709fe814898237 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-giga-test-full This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 0.0 | 49548a71dd43bdb4be244f7192760c26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 11791 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 ... | 809ad0635b42381c619b7c5e97ac31bd |
apache-2.0 | ['translation'] | false | opus-mt-fr-ig * source languages: fr * target languages: ig * OPUS readme: [fr-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ig/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 1dd6aa603cd59366b3f804dac74a525f |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-ner-conll2003 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: 0.0355 - Precision: 0.9438 - Recall: 0.9525 - F1: 0.9482 - Accuracy: 0.9911 | 9e6288e79aa6bf0e206b7e26d09f970d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 - mixed_precision_t... | b6ec862e9f161013cc3bac65a6a2bb7e |
apache-2.0 | ['tapas', 'TapasModel'] | false | TAPAS base model This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the ... | 36e3a6eadd88e8bafcde66a22413859e |
apache-2.0 | ['tapas', 'TapasModel'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, a... | d46905c16f33e571e3716e92c07327a9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1391 - F1: 0.8626 | a60a727707db4962f58744d889fd2e38 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 525 | 0.1675 | 0.8188 | | No log | 2.0 | 1050 | 0.1388 | 0.8399 | | No log | 3.0 | 1575 | 0.1391 | 0.8626 | ... | f051636be50c642895ce11028e0a36e8 |
cc-by-4.0 | ['generated_from_trainer'] | false | norbert2-finetuned-comments This model is a fine-tuned version of [ltgoslo/norbert2](https://huggingface.co/ltgoslo/norbert2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8562 | 89a3a42ed816df752b08571a5df82fdd |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 17 - eval_batch_size: 17 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 8874cc68db9a61ec9dbab8aa55608728 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7115 | 1.0 | 1030 | 3.2753 | | 3.248 | 2.0 | 2060 | 3.0974 | | 3.0825 | 3.0 | 3090 | 3.0759 | | 2.992 | 4.0 | 4120 | 3.0478 ... | 41e62e94d57f66b62075777813214a5e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-conformer-rel-pos-large-960h-ft-speech_commands This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large-960h-ft) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6823 - Ac... | 52c0f7b0873cd4ffc6c4baa2cc74e841 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 62a1912a2120ba7857c855a18111f2c8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.554 | 1.0 | 165 | 2.2488 | 0.8784 | | 1.645 | 2.0 | 330 | 1.3390 | 0.9100 | | 1.2462 | 3.0 | 495 | 0.9315 | 0.... | 7fc8daf0cfd599d067cebb7020c42f59 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | cleed221 Dreambooth model trained by jtwinfree44 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-stable-... | d10b864e2d415609d6c303397fa544e0 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | jleonart Dreambooth model trained by Jorgeleon 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-stable-di... | 1ada61a9e15e5655c6064cc779b84f60 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 1600k (uncased) Seed 3 intermediate checkpoint 1600k 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... | ffb85a4d969d1fa683c7c6ba35e2047d |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | 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-3-1600k') model = BertModel.from_pretrained("multiberts-seed-3-1600k") text = "Replace me by any text you'd lik... | 7ee0c53c46ffa03ecab386c025cadd6a |
apache-2.0 | ['generated_from_trainer'] | false | training This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the cynthiachan/FeedRef2022 dataset. It achieves the following results on the evaluation set: - Loss: 0.0884 - Attackid Precision: 0.7429 - Attackid Recall: 1.0 - Attackid F1... | 65f11d7eebea66850e9cf9de014e3d7f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Attackid F1 | Attackid Number | Attackid Precision | Attackid Recall | Bitcoinaddr F1 | Bitcoinaddr Number | Bitcoinaddr Precision | Bitcoinaddr Recall | Cve F1 | Cve Number | Cve Precision | Cve Recall | Defenderthreat F1 | Defenderthreat Number | Defenderthreat Pre... | bac352847cf3971a7a94eacca685ac99 |
apache-2.0 | ['monai', 'medical'] | false | Model Overview A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation). | 2e7f7b20e523bb9822c9532c25cb188f |
apache-2.0 | ['monai', 'medical'] | false | Workflow The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR). - The ET is described by areas that show hyper intensity in T1c when compared to T1, but al... | 38a63818853529202bb71017208a4d59 |
apache-2.0 | ['monai', 'medical'] | false | Data The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html). - Target: 3 tumor subregions - Task: Segmentation - Modality: MRI - Size: 285 3D volumes (4 channels each) The provided labelled data was partitioned, based on our own... | 438146035b53938776bff3281fa4cbc4 |
apache-2.0 | ['monai', 'medical'] | false | Training configuration This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following: - GPU: At least 16GB of GPU memory. - Actual Model Input: 224 x 224 x 144 - AMP: Tru... | f62d0c968ff93f94a12174bf6af46dfa |
apache-2.0 | ['monai', 'medical'] | false | Input Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm) 1. Normalizing to unit std with zero mean 2. Randomly cropping to (224, 224, 144) 3. Randomly spatial flipping 4. Randomly scaling and shifting intensity of the volume | 9a2dec24d0671e2f1e7d0e1207b13e21 |
apache-2.0 | ['monai', 'medical'] | false | References [1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654. | 76e9506cf28cb7164357b11cc0b9738f |
apache-2.0 | ['fill-mask'] | false | 90% Sparse BERT-Base (uncased) Prune OFA This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021. For further details on the model and its result, see our paper and our implementation available [here](htt... | 13fa58fd28df4f34422d6309c90a9d12 |
cc-by-4.0 | ['norwegian', 'bert'] | false | Description NB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway. This model is based on the same structure as [BERT Cased multilingual model](https://github.com/google-research/bert/blob/master/multilingual.md), and is trained on a wide variety of Norwegi... | 96e483ea470598e36aee95fb795224e7 |
cc-by-4.0 | ['norwegian', 'bert'] | false | Intended use & limitations The 1.1 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on GitHub, see * https://github.com/NBAiLab/notram | 37f70f2f0daadeb2383792231e0ec147 |
apache-2.0 | ['finnish', 'roberta'] | false | RoBERTa large model for Finnish Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model i... | f5d8e03858b06a7f7986e17cb53077f4 |
apache-2.0 | ['finnish', 'roberta'] | false | Model description Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to gener... | 5987792bfaf0428b598fd1169c304869 |
apache-2.0 | ['finnish', 'roberta'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification,... | b2e39b9363fe8fa555d21d8ee837b192 |
apache-2.0 | ['finnish', 'roberta'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-finnish') >>> unmasker("Moikka olen <mask> kielimalli.") [{'sequence': 'Moikka olen hyvä kielimalli.', 'sco... | 0b04c14d05a8178f946a4615444360a1 |
apache-2.0 | ['finnish', 'roberta'] | false | Training data This Finnish RoBERTa model was pretrained on the combination of five datasets: - [mc4](https://huggingface.co/datasets/mc4), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset - [wikipedia](https://huggingface.co/... | 1b1eb89612b690ed4b534b2d19448918 |
apache-2.0 | ['finnish', 'roberta'] | false | Preprocessing The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked with `<s>` and the end of one by `</s>` The details of the masking... | 42d79057575447d891c5efc77b48c03a |
apache-2.0 | ['finnish', 'roberta'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1... | 045b067652016a91ed0ddf5c97ab91a2 |
apache-2.0 | ['finnish', 'roberta'] | false | Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequ... | daf71f85ef477dd70d128363cfc99a97 |
apache-2.0 | ['finnish', 'roberta'] | false | Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) - Tommi Vehviläinen [Hugging Face... | 6c71096cb34debd0608c637616aad3e6 |
cc-by-sa-4.0 | [] | false | yacis-electra-small This is [ELECTRA](https://github.com/google-research/electra) Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of [YACIS](https://github.com/ptaszynski/yacis-corpus) blog corpus. The corpus was tokenized for pretraining with [MeCab](https://taku910.github.io/mecab/... | 3a394c7c75202c7983be55d98b194ed4 |
cc-by-sa-4.0 | [] | false | Training data and libraries YACIS-ELECTRA is trained on the whole of [YACIS](https://github.com/ptaszynski/yacis-corpus) blog corpus, which is a Japanese blog corpus containing 5.6 billion words in 354 million sentences. The corpus was originally split into sentences using custom rules, and each sentence was tokeniz... | 4fba47ea94a1e56fa0392548cb6c0469 |
cc-by-sa-4.0 | [] | false | Licenses The pretrained model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" sty... | 05549569ffd7814963fc94f13a8725a3 |
cc-by-sa-4.0 | [] | false | Citations Please, cite the model using the following citation. ``` @inproceedings{shibata2022yacis-electra, title={日本語大規模ブログコーパスYACISに基づいたELECTRA事前学習済み言語モデルの作成及び性能評価}, % title={Development and performance evaluation of ELECTRA pretrained language model based on YACIS large-scale Japanese blog corpus [in Japanese... | e1367f31e4e7af8dfdca7e5975e529ef |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.01_pretrainedFalse_epochs3 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.2579 | 4f3b8363893cc4d6b3b6b77d69e0b887 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6821 | 1.0 | 1323 | 3.2535 | | 2.5045 | 2.0 | 2646 | 3.2502 | | 2.4511 | 3.0 | 3969 | 3.2579 | | b240caf3a0b622520b7c1287f690aef4 |
apache-2.0 | ['generated_from_keras_callback'] | false | muhtasham/bert-tiny-finetuned-finer-tf 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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0372 - Validation Loss: 0.0296 - Epoch: 2 | 556fb7e77e1626bc54358cde11043124 |
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': 2e-05, 'decay_steps': 168822, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | 7c1f77e7202aaed6cc8e308445ba831f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1188 | 0.0420 | 0 | | 0.0438 | 0.0313 | 1 | | 0.0372 | 0.0296 | 2 | | 2139ecfb7712b8713be95358c41d4a2f |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-it_s557 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 2175cccd21e98d1809baf4eca0797319 |
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