license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Article_50v1_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4554 - Precision: 0.2880 - Recall: 0.1268 - F1: 0.1761 - Accuracy: 0.83... | a4acd1040ca46fc3f316b6a72570d2be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.5376 | 0.1806 | 0.0259 | 0.0453 | 0.7947 | | No log | 2.0 |... | db0b1a0594e7d9a414c4fc1f0f0459df |
apache-2.0 | ['generated_from_keras_callback'] | false | oyk100/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5231 - Validation Loss: 0.4828 - Train Matthews Correlation: 0.4... | a0ee3a3be0c93caeddc5bb8f9eefe79c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False... | 302720ac247241a3fa7f478c5c36a116 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5231 | 0.4828 | 0.4484 | 0 | | fc574b35762395286182950ecb767949 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.3930 - F1: 0.6815 | 70922cebceb8e2baced44e381e2594e4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1519 | 1.0 | 50 | 0.5744 | 0.5124 | | 0.5155 | 2.0 | 100 | 0.4160 | 0.6214 | | 0.3623 | 3.0 | 150 | 0.3930 | 0.6815 | ... | d779114226ae5fe25d0110b87ecae0cd |
mit | ['generated_from_trainer'] | false | training This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the cynthiachan/FeedRef_10pct dataset. It achieves the following results on the evaluation set: - Loss: 0.1033 - Attackid Precision: 1.0 - Attackid Recall: 1.0 - Attackid F1: 1.0 - Attackid Number: 6 - Cve Precision:... | 150b2c388bc1fa597a645114dc7ac0cb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall ... | 029cccdd74a0d82657b26a2689bb7d5c |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/p... | adbd6d8cadc485580024cfcb559a93ef |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-japanese-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[model.config... | 501fb1ae01a7ba9a7c25868f7928c121 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-ms-test-3 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4238 - Accuracy: 0.8861 | 0905743a11f2b53714656d9e2ec4c718 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.91 | 5 | 0.6537 | 0.8228 | | 0.8014 | 1.91 | 10 | 0.4649 | 0.8481 | | 0.8014 | 2.91 | 15 | 0.4238 | 0.... | 682497fd9c155a6de7483bfb2a31ed3b |
apache-2.0 | ['translation'] | false | opus-mt-fi-crs * source languages: fi * target languages: crs * OPUS readme: [fi-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-crs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 3ed44287863a93edea204031c5d94390 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-es_s474 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | efeb09e3345e1974f1363eb20bf84117 |
creativeml-openrail-m | ['text-to-image'] | false | model by hjguo This your the Stable Diffusion model fine-tuned the hog-rider concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks character** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | 13b3cdc02f5630858796eb2ebe70b5c6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Hindi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2609 - Wer: 10.4134 | 80cb18f0bf49c3b9ecd7e1af8de55caf |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 6.11 | 5000 | 0.2609 | 10.4134 | | 4a68914e675cb693d0ede6179891d79e |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.2898 | fbcf19072b996e4b937f38371d448aeb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.2965 | 0.7 | 500 | 6.5781 | | 6.4256 | 1.39 | 1000 | 6.4717 | | 6.184 | 2.09 | 1500 | 6.2234 | | 5.8599 | 2.78 | 2000 | 6.2671 ... | dffaf020132d66cc6def8a9980cdf664 |
cc-by-4.0 | ['spanish', 'roberta'] | false | This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding mo... | 60d5923b77b02cb5bc41b2fc03076284 |
apache-2.0 | ['generated_from_keras_callback'] | false | lakshaywadhwa1993/mt5-base-finetuned-hindi-mt5-base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1571 - Validation Loss: 1.0867 - Epoch: 4 | e55bdecbab2478b772f492866f205bc6 |
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': 5.6e-05, 'decay_steps': 61500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | 1e41e5e59eb5601786a97d98657d5ac9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1142 | 1.2829 | 0 | | 1.4055 | 1.1648 | 1 | | 1.2713 | 1.1204 | 2 | | 1.2016 | 1.0934 | 3 | | 1.1571 | 1.0867 | 4 | | f8f3234e659c500e21855d5e86632e01 |
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