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: 0.8302 | 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 | 58 | 0.4930 | 0.2471 | 0.0789 | 0.1196 | 0.8157 | | No log | 3.0 | 87 | 0.4554 | 0.2880 | 0.1268 | 0.1761 | 0.8302 | | 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: 0.4484 - Epoch: 0 | 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, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 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: 1.0 - Cve Recall: 1.0 - Cve F1: 1.0 - Cve Number: 11 - Defenderthreat Precision: 0.0 - Defenderthreat Recall: 0.0 - Defenderthreat F1: 0.0 - Defenderthreat Number: 2 - Domain Precision: 0.8636 - Domain Recall: 0.8261 - Domain F1: 0.8444 - Domain Number: 23 - Email Precision: 1.0 - Email Recall: 1.0 - Email F1: 1.0 - Email Number: 3 - Filepath Precision: 0.8108 - Filepath Recall: 0.9091 - Filepath F1: 0.8571 - Filepath Number: 165 - Hostname Precision: 0.9231 - Hostname Recall: 1.0 - Hostname F1: 0.9600 - Hostname Number: 12 - Ipv4 Precision: 0.9167 - Ipv4 Recall: 0.9167 - Ipv4 F1: 0.9167 - Ipv4 Number: 12 - Md5 Precision: 0.875 - Md5 Recall: 0.9423 - Md5 F1: 0.9074 - Md5 Number: 52 - Sha1 Precision: 0.75 - Sha1 Recall: 0.8571 - Sha1 F1: 0.8000 - Sha1 Number: 7 - Sha256 Precision: 0.8 - Sha256 Recall: 1.0 - Sha256 F1: 0.8889 - Sha256 Number: 44 - Uri Precision: 0.0 - Uri Recall: 0.0 - Uri F1: 0.0 - Uri Number: 1 - Overall Precision: 0.8383 - Overall Recall: 0.9201 - Overall F1: 0.8773 - Overall Accuracy: 0.9816 | 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 | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4353 | 0.37 | 500 | 0.3525 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.3984 | 0.6182 | 0.4846 | 165 | 0.0714 | 0.3333 | 0.1176 | 12 | 0.0 | 0.0 | 0.0 | 12 | 0.8936 | 0.8077 | 0.8485 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.4937 | 0.8864 | 0.6341 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.4156 | 0.5533 | 0.4746 | 0.9459 | | 0.2089 | 0.75 | 1000 | 0.1812 | 0.0 | 0.0 | 0.0 | 6 | 0.9 | 0.8182 | 0.8571 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.15 | 0.2609 | 0.1905 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.6432 | 0.7758 | 0.7033 | 165 | 0.0 | 0.0 | 0.0 | 12 | 0.6471 | 0.9167 | 0.7586 | 12 | 0.7143 | 0.8654 | 0.7826 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.5286 | 0.8409 | 0.6491 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.5315 | 0.6982 | 0.6036 | 0.9626 | | 0.1453 | 1.12 | 1500 | 0.1374 | 0.75 | 0.5 | 0.6 | 6 | 0.9167 | 1.0 | 0.9565 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.5135 | 0.8261 | 0.6333 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.6863 | 0.8485 | 0.7588 | 165 | 0.7 | 0.5833 | 0.6364 | 12 | 0.6667 | 0.6667 | 0.6667 | 12 | 0.8167 | 0.9423 | 0.8750 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8333 | 0.9091 | 0.8696 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7048 | 0.8195 | 0.7579 | 0.9745 | | 0.1277 | 1.5 | 2000 | 0.1400 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.7273 | 0.6957 | 0.7111 | 23 | 0.2 | 0.3333 | 0.25 | 3 | 0.7181 | 0.8182 | 0.7649 | 165 | 0.9167 | 0.9167 | 0.9167 | 12 | 0.7857 | 0.9167 | 0.8462 | 12 | 0.8167 | 0.9423 | 0.8750 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8302 | 1.0 | 0.9072 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7634 | 0.8402 | 0.8000 | 0.9735 | | 0.1074 | 1.87 | 2500 | 0.1101 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.72 | 0.7826 | 0.7500 | 23 | 0.2857 | 0.6667 | 0.4 | 3 | 0.7554 | 0.8424 | 0.7966 | 165 | 0.8571 | 1.0 | 0.9231 | 12 | 0.8182 | 0.75 | 0.7826 | 12 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6833 | 0.9318 | 0.7885 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7660 | 0.8521 | 0.8067 | 0.9762 | | 0.0758 | 2.25 | 3000 | 0.1161 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.9091 | 0.8696 | 0.8889 | 23 | 0.5 | 0.6667 | 0.5714 | 3 | 0.8251 | 0.9152 | 0.8678 | 165 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 0.6667 | 0.8 | 12 | 0.9259 | 0.9615 | 0.9434 | 52 | 1.0 | 0.5714 | 0.7273 | 7 | 0.8958 | 0.9773 | 0.9348 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8722 | 0.9083 | 0.8899 | 0.9814 | | 0.064 | 2.62 | 3500 | 0.1275 | 1.0 | 1.0 | 1.0 | 6 | 0.8333 | 0.9091 | 0.8696 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.8947 | 0.7391 | 0.8095 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8418 | 0.9030 | 0.8713 | 165 | 0.8571 | 1.0 | 0.9231 | 12 | 1.0 | 0.75 | 0.8571 | 12 | 0.9245 | 0.9423 | 0.9333 | 52 | 0.6667 | 0.5714 | 0.6154 | 7 | 0.8113 | 0.9773 | 0.8866 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8580 | 0.8935 | 0.8754 | 0.9793 | | 0.0522 | 3.0 | 4000 | 0.1033 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.8636 | 0.8261 | 0.8444 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8108 | 0.9091 | 0.8571 | 165 | 0.9231 | 1.0 | 0.9600 | 12 | 0.9167 | 0.9167 | 0.9167 | 12 | 0.875 | 0.9423 | 0.9074 | 52 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8 | 1.0 | 0.8889 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8383 | 0.9201 | 0.8773 | 0.9816 | | 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/pos/) (Universal Part-Of-Speech). | 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.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(s,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-base-japanese-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` | 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 | 0.8861 | | 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](https://object.pouta.csc.fi/OPUS-MT-models/fi-crs/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-crs/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-crs/opus-2020-01-08.eval.txt) | 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:     | 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 | | 5.895 | 3.48 | 2500 | 6.2723 | | 5.7225 | 4.17 | 3000 | 6.2898 | | 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 more often documents with very large values (poor quality) of very small values (short, repetitive texts). This model takes the one using [sequence length 128](https://huggingface.co/bertin-project/bertin-base-gaussian) and trains during 25.000 steps using sequence length 512. Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information. This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. | 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}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 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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