Datasets:
davanstrien
/
model_cards_with_long_context_embeddings

Dataset card Data Studio Files
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gxhuggingface/distilbert-base-uncased-finetuned-emotion
2023-05-15T16:28:45.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
gxhuggingface
null
null
gxhuggingface/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-15T15:42:00
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9405 - name: F1 type: f1 value: 0.9406663459684013 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1472 - Accuracy: 0.9405 - F1: 0.9407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.1786 | 0.9275 | 0.9274 | | No log | 2.0 | 500 | 0.1472 | 0.9405 | 0.9407 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
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nimeeshachan/mlma_nchan19_biogpt_on_adr_test_set
2023-05-15T17:17:25.000Z
[ "transformers", "pytorch", "tensorboard", "gpt2", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
token-classification
nimeeshachan
null
null
nimeeshachan/mlma_nchan19_biogpt_on_adr_test_set
0
2
transformers
2023-05-15T16:05:09
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: mlma_nchan19_biogpt_on_adr_test_set results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mlma_nchan19_biogpt_on_adr_test_set This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1452 - Precision: 0.4772 - Recall: 0.5467 - F1: 0.5096 - Accuracy: 0.9523 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 448 | 0.1998 | 0.4210 | 0.3185 | 0.3626 | 0.9382 | | 0.289 | 2.0 | 896 | 0.1630 | 0.4394 | 0.5043 | 0.4696 | 0.9474 | | 0.1587 | 3.0 | 1344 | 0.1452 | 0.4772 | 0.5467 | 0.5096 | 0.9523 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
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yeobeom/distilbert-base-uncased-finetuned-emotion
2023-05-15T17:17:30.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
yeobeom
null
null
yeobeom/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-15T17:12:49
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9256616841507974 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2219 - Accuracy: 0.9255 - F1: 0.9257 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8632 | 1.0 | 250 | 0.3232 | 0.906 | 0.9035 | | 0.2592 | 2.0 | 500 | 0.2219 | 0.9255 | 0.9257 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,848
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livinNector/IndicBERTv2-MLM-Sam-TLM-NER
2023-05-18T13:44:09.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
livinNector
null
null
livinNector/IndicBERTv2-MLM-Sam-TLM-NER
0
2
transformers
2023-05-15T17:56:40
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: IndicBERTv2-MLM-Sam-TLM-NER results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IndicBERTv2-MLM-Sam-TLM-NER This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-Sam-TLM](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4521 - Precision: 0.7629 - Recall: 0.7792 - F1: 0.7710 - Accuracy: 0.9038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3268 | 0.49 | 1000 | 0.3440 | 0.7207 | 0.7602 | 0.7399 | 0.8887 | | 0.2763 | 0.99 | 2000 | 0.3083 | 0.7568 | 0.7732 | 0.7649 | 0.8983 | | 0.2604 | 1.48 | 3000 | 0.3312 | 0.7309 | 0.7494 | 0.7401 | 0.8909 | | 0.2501 | 1.98 | 4000 | 0.3017 | 0.7415 | 0.7956 | 0.7676 | 0.9014 | | 0.2269 | 2.47 | 5000 | 0.2930 | 0.7528 | 0.7970 | 0.7743 | 0.9050 | | 0.223 | 2.96 | 6000 | 0.2963 | 0.7590 | 0.7963 | 0.7772 | 0.9053 | | 0.2011 | 3.46 | 7000 | 0.2939 | 0.7627 | 0.7946 | 0.7783 | 0.9079 | | 0.1999 | 3.95 | 8000 | 0.3036 | 0.7676 | 0.7903 | 0.7788 | 0.9069 | | 0.1815 | 4.44 | 9000 | 0.3125 | 0.7618 | 0.7915 | 0.7764 | 0.9056 | | 0.1777 | 4.94 | 10000 | 0.3083 | 0.7748 | 0.7957 | 0.7851 | 0.9098 | | 0.1622 | 5.43 | 11000 | 0.3251 | 0.7721 | 0.7909 | 0.7814 | 0.9089 | | 0.1598 | 5.93 | 12000 | 0.3197 | 0.7767 | 0.7947 | 0.7856 | 0.9092 | | 0.145 | 6.42 | 13000 | 0.3366 | 0.7718 | 0.7986 | 0.7850 | 0.9101 | | 0.1436 | 6.91 | 14000 | 0.3247 | 0.7776 | 0.7977 | 0.7875 | 0.9112 | | 0.1306 | 7.41 | 15000 | 0.3502 | 0.7779 | 0.7958 | 0.7867 | 0.9107 | | 0.1311 | 7.9 | 16000 | 0.3585 | 0.7857 | 0.7909 | 0.7883 | 0.9105 | | 0.12 | 8.4 | 17000 | 0.3717 | 0.7768 | 0.7911 | 0.7839 | 0.9099 | | 0.1202 | 8.89 | 18000 | 0.3667 | 0.7796 | 0.7882 | 0.7839 | 0.9100 | | 0.1141 | 9.38 | 19000 | 0.3860 | 0.7857 | 0.7900 | 0.7879 | 0.9100 | | 0.1113 | 9.88 | 20000 | 0.3824 | 0.7758 | 0.7970 | 0.7862 | 0.9094 | | 0.1056 | 10.37 | 21000 | 0.4041 | 0.7740 | 0.7952 | 0.7845 | 0.9084 | | 0.1073 | 10.86 | 22000 | 0.4062 | 0.7735 | 0.7929 | 0.7831 | 0.9094 | | 0.1063 | 11.36 | 23000 | 0.4197 | 0.7720 | 0.7866 | 0.7793 | 0.9071 | | 0.1026 | 11.85 | 24000 | 0.4179 | 0.7625 | 0.7767 | 0.7695 | 0.9040 | | 0.1042 | 12.35 | 25000 | 0.4392 | 0.7639 | 0.7748 | 0.7693 | 0.9037 | | 0.101 | 12.84 | 26000 | 0.4373 | 0.7533 | 0.7795 | 0.7662 | 0.9029 | | 0.1003 | 13.33 | 27000 | 0.4554 | 0.7535 | 0.7774 | 0.7653 | 0.9021 | | 0.0993 | 13.83 | 28000 | 0.4530 | 0.7555 | 0.7773 | 0.7663 | 0.9019 | | 0.0978 | 14.32 | 29000 | 0.4467 | 0.7637 | 0.7843 | 0.7738 | 0.9050 | | 0.0946 | 14.81 | 30000 | 0.4521 | 0.7629 | 0.7792 | 0.7710 | 0.9038 | ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
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harshadpc10/MyFirstModel
2023-05-15T20:56:27.000Z
[ "keras", "region:us" ]
null
harshadpc10
null
null
harshadpc10/MyFirstModel
0
2
keras
2023-05-15T18:39:46
--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: | Hyperparameters | Value | | :-- | :-- | | name | SGD | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | False | | is_legacy_optimizer | False | | learning_rate | 0.009999999776482582 | | momentum | 0.0 | | nesterov | False | | training_precision | float32 |
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YaYaB/l3-setfit_v2
2023-05-15T21:37:13.000Z
[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
YaYaB
null
null
YaYaB/l3-setfit_v2
0
2
sentence-transformers
2023-05-15T21:37:09
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # YaYaB/l3-setfit_v2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("YaYaB/l3-setfit_v2") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
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Madhu45/Teledermatology_model
2023-05-16T07:23:31.000Z
[ "keras", "region:us" ]
null
Madhu45
null
null
Madhu45/Teledermatology_model
0
2
keras
2023-05-15T22:06:45
--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: | Hyperparameters | Value | | :-- | :-- | | name | SGD | | learning_rate | 0.05000000074505806 | | decay | 0.0 | | momentum | 0.0 | | nesterov | False | | training_precision | float32 |
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TimBless222/distilbert-base-uncased-finetuned-emotion
2023-05-19T15:24:14.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
TimBless222
null
null
TimBless222/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-16T00:04:29
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9285439912301902 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.9285 - F1: 0.9285 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8381 | 1.0 | 250 | 0.3165 | 0.9075 | 0.9040 | | 0.2524 | 2.0 | 500 | 0.2183 | 0.9285 | 0.9285 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
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vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridadDA-V3
2023-05-16T03:17:07.000Z
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
vg055
null
null
vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridadDA-V3
0
2
transformers
2023-05-16T01:02:19
--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridadDA-V3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridadDA-V3 This model is a fine-tuned version of [vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation](https://huggingface.co/vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6583 - F1: 0.7400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5919 | 1.0 | 17166 | 0.5992 | 0.7388 | | 0.3925 | 2.0 | 34332 | 0.6583 | 0.7400 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
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yyabuki/distilbert-base-uncased-finetuned-emotion
2023-05-17T09:54:24.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
yyabuki
null
null
yyabuki/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-16T01:17:15
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.925439015968626 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2205 - Accuracy: 0.9255 - F1: 0.9254 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8201 | 1.0 | 250 | 0.3106 | 0.907 | 0.9049 | | 0.2487 | 2.0 | 500 | 0.2205 | 0.9255 | 0.9254 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.12.0 - Datasets 2.11.0 - Tokenizers 0.13.3
1,842
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AustinCarthy/MixGPT2_10K_fromB_BFall_10KGen_topP_0.75
2023-05-18T20:17:54.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/MixGPT2_10K_fromB_BFall_10KGen_topP_0.75
0
2
transformers
2023-05-16T03:36:29
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_10K_fromB_BFall_10KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_10K_fromB_BFall_10KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0493 - Accuracy: 0.9952 - F1: 0.9474 - Precision: 0.9989 - Recall: 0.901 - Roc Auc Score: 0.9505 - Tpr At Fpr 0.01: 0.9106 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0068 | 1.0 | 13125 | 0.0321 | 0.9930 | 0.9209 | 0.9949 | 0.8572 | 0.9285 | 0.821 | | 0.0041 | 2.0 | 26250 | 0.0398 | 0.9941 | 0.9341 | 0.9973 | 0.8784 | 0.9391 | 0.8602 | | 0.0011 | 3.0 | 39375 | 0.0646 | 0.9922 | 0.9109 | 0.9990 | 0.837 | 0.9185 | 0.8694 | | 0.0014 | 4.0 | 52500 | 0.0567 | 0.9929 | 0.9191 | 0.9998 | 0.8504 | 0.9252 | 0.895 | | 0.0 | 5.0 | 65625 | 0.0493 | 0.9952 | 0.9474 | 0.9989 | 0.901 | 0.9505 | 0.9106 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
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AustinCarthy/MixGPT2_10K_fromB_BFall_20KGen_topP_0.75
2023-05-16T15:28:14.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/MixGPT2_10K_fromB_BFall_20KGen_topP_0.75
0
2
transformers
2023-05-16T05:04:51
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_10K_fromB_BFall_20KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_10K_fromB_BFall_20KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0667 - Accuracy: 0.9940 - F1: 0.9325 - Precision: 0.9993 - Recall: 0.874 - Roc Auc Score: 0.9370 - Tpr At Fpr 0.01: 0.8984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0038 | 1.0 | 19688 | 0.0511 | 0.9926 | 0.9158 | 0.9991 | 0.8454 | 0.9227 | 0.8744 | | 0.0028 | 2.0 | 39376 | 0.0423 | 0.9946 | 0.9405 | 0.9951 | 0.8916 | 0.9457 | 0.884 | | 0.0006 | 3.0 | 59064 | 0.0510 | 0.9940 | 0.9325 | 0.9975 | 0.8754 | 0.9376 | 0.875 | | 0.0 | 4.0 | 78752 | 0.0355 | 0.9958 | 0.9536 | 0.9987 | 0.9124 | 0.9562 | 0.9172 | | 0.0 | 5.0 | 98440 | 0.0667 | 0.9940 | 0.9325 | 0.9993 | 0.874 | 0.9370 | 0.8984 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,241
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mathislucka/bi-deberta-base-hallucination-v1
2023-05-16T06:28:21.000Z
[ "sentence-transformers", "pytorch", "deberta-v2", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]
sentence-similarity
mathislucka
null
null
mathislucka/bi-deberta-base-hallucination-v1
0
2
sentence-transformers
2023-05-16T06:24:17
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 516 with parameters: ``` {'batch_size': 14} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 300, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
3,805
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neurae/bert-dnd-intents
2023-07-22T04:36:32.000Z
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
neurae
null
null
neurae/bert-dnd-intents
0
2
transformers
2023-05-16T06:37:46
--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is bert base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. | parametrs | value | |---------------|----------| | learning rate | 1.3e-4 | | lr scheduler | constant | | weight decay | 7e-2 | Model has next metrics on test data from dataset | metric | value | |----------|-------| | accuracy | 0.978 | | Macro F1 | 0.977 | | Micro F1 | 0.978 |
542
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neurae/distilbert-dnd-intents
2023-07-16T09:37:51.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
neurae
null
null
neurae/distilbert-dnd-intents
0
2
transformers
2023-05-16T06:39:30
--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is distilbert base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. | parametrs | value | |---------------|----------| | learning rate | 1.8e-4 | | lr scheduler | linear | | weight decay | 0 | Model has next metrics on test data from dataset | metric | value | |----------|-------| | accuracy | 0.985 | | Macro F1 | 0.984 | | Micro F1 | 0.985 |
548
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AustinCarthy/MixGPT2_10K_fromB_BFall_30KGen_topP_0.75
2023-05-16T15:41:05.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/MixGPT2_10K_fromB_BFall_30KGen_topP_0.75
0
2
transformers
2023-05-16T07:12:31
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_10K_fromB_BFall_30KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_10K_fromB_BFall_30KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0617 - Accuracy: 0.9926 - F1: 0.9162 - Precision: 0.9998 - Recall: 0.8456 - Roc Auc Score: 0.9228 - Tpr At Fpr 0.01: 0.8956 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.005 | 1.0 | 26250 | 0.0392 | 0.9921 | 0.9101 | 0.9983 | 0.8362 | 0.9181 | 0.838 | | 0.0015 | 2.0 | 52500 | 0.0749 | 0.9909 | 0.8940 | 0.9978 | 0.8098 | 0.9049 | 0.8144 | | 0.0007 | 3.0 | 78750 | 0.0421 | 0.9952 | 0.9471 | 0.9989 | 0.9004 | 0.9502 | 0.9072 | | 0.0013 | 4.0 | 105000 | 0.0393 | 0.9941 | 0.9344 | 0.9998 | 0.877 | 0.9385 | 0.9138 | | 0.0003 | 5.0 | 131250 | 0.0617 | 0.9926 | 0.9162 | 0.9998 | 0.8456 | 0.9228 | 0.8956 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
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alessandrobrra/dqn-BeamRiderNoFrameskip-v4
2023-05-16T08:06:24.000Z
[ "stable-baselines3", "BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
alessandrobrra
null
null
alessandrobrra/dqn-BeamRiderNoFrameskip-v4
0
2
stable-baselines3
2023-05-16T08:05:24
--- library_name: stable-baselines3 tags: - BeamRiderNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BeamRiderNoFrameskip-v4 type: BeamRiderNoFrameskip-v4 metrics: - type: mean_reward value: 602.00 +/- 173.17 name: mean_reward verified: false --- # **DQN** Agent playing **BeamRiderNoFrameskip-v4** This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga alessandrobrra -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga alessandrobrra -f logs/ python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga alessandrobrra ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
2,666
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neurae/roberta-dnd-intents
2023-07-16T09:33:44.000Z
[ "transformers", "pytorch", "roberta", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
neurae
null
null
neurae/roberta-dnd-intents
0
2
transformers
2023-05-16T09:18:00
--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is roberta base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. | parametrs | value | |---------------|----------| | learning rate | 5e-5 | | lr scheduler | linear | | weight decay | 0 | Model has next metrics on test data from dataset | metric | value | |----------|-------| | accuracy | 0.985 | | Macro F1 | 0.985 | | Micro F1 | 0.985 |
545
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platzi/platzi-distilroberta-base-mrpc-glue-jonathan-narvaez
2023-05-16T11:21:15.000Z
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
platzi
null
null
platzi/platzi-distilroberta-base-mrpc-glue-jonathan-narvaez
0
2
transformers
2023-05-16T09:18:27
--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: platzi-distilroberta-base-mrpc-glue-jonathan-narvaez results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8259803921568627 - name: F1 type: f1 value: 0.8725314183123878 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-distilroberta-base-mrpc-glue-jonathan-narvaez This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4482 - Accuracy: 0.8260 - F1: 0.8725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3682 | 1.09 | 500 | 0.4482 | 0.8260 | 0.8725 | | 0.3611 | 2.18 | 1000 | 0.4482 | 0.8260 | 0.8725 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
2,437
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AustinCarthy/MixGPT2_10K_fromB_BFall_40KGen_topP_0.75
2023-05-18T20:30:34.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/MixGPT2_10K_fromB_BFall_40KGen_topP_0.75
0
2
transformers
2023-05-16T09:58:32
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_10K_fromB_BFall_40KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_10K_fromB_BFall_40KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0480 - Accuracy: 0.9944 - F1: 0.9378 - Precision: 0.9998 - Recall: 0.883 - Roc Auc Score: 0.9415 - Tpr At Fpr 0.01: 0.91 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0059 | 1.0 | 32813 | 0.0405 | 0.9934 | 0.9250 | 0.9991 | 0.8612 | 0.9306 | 0.8928 | | 0.0036 | 2.0 | 65626 | 0.0503 | 0.9929 | 0.9193 | 0.9998 | 0.8508 | 0.9254 | 0.8914 | | 0.001 | 3.0 | 98439 | 0.0706 | 0.9908 | 0.8936 | 0.9995 | 0.808 | 0.9040 | 0.8702 | | 0.0011 | 4.0 | 131252 | 0.0564 | 0.9943 | 0.9363 | 0.9986 | 0.8812 | 0.9406 | 0.8958 | | 0.0 | 5.0 | 164065 | 0.0480 | 0.9944 | 0.9378 | 0.9998 | 0.883 | 0.9415 | 0.91 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,246
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neurae/albert-dnd-intents
2023-07-16T09:38:16.000Z
[ "transformers", "pytorch", "albert", "text-classification", "en", "dataset:neurae/dnd_style_intents", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
neurae
null
null
neurae/albert-dnd-intents
0
2
transformers
2023-05-16T09:58:57
--- datasets: - neurae/dnd_style_intents language: - en pipeline_tag: text-classification license: apache-2.0 metrics: - accuracy - f1 --- This is albert base tuned with optimal lr, lr scheduler and weight decay on dnd-style-intents dataset. | parametrs | value | |---------------|----------| | learning rate | 5e-5 | | lr scheduler | linear | | weight decay | 0 | Model has next metrics on test data from dataset | metric | value | |----------|-------| | accuracy | 0.981 | | Macro F1 | 0.979 | | Micro F1 | 0.985 |
544
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Yorth/gpt2_medium_poetry
2023-05-16T13:09:09.000Z
[ "transformers", "pytorch", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
Yorth
null
null
Yorth/gpt2_medium_poetry
0
2
transformers
2023-05-16T12:38:00
--- tags: - generated_from_keras_callback model-index: - name: gpt2_medium_poetry results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2_medium_poetry This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.29.1 - TensorFlow 2.12.0 - Tokenizers 0.13.3
854
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Chantland/HRAF_EVENT_Demo
2023-06-26T20:19:53.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "anthropology", "license:unlicense", "endpoints_compatible", "region:us" ]
text-classification
Chantland
null
null
Chantland/HRAF_EVENT_Demo
0
2
transformers
2023-05-16T15:25:31
--- license: unlicense tags: - anthropology - text-classification --- Text classification model used to decode passages that contain misfortunate events. Current F1 score of 140 passages not used for training is .94. <br><br><br> For a quick demo, try typing in a sentence or even a paragraph in the <b>Hosted inference API</b> then pressing "compute"!
353
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2rtl3/mn-roberta-base-demo-named-entity
2023-05-16T16:55:52.000Z
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "mn", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2rtl3
null
null
2rtl3/mn-roberta-base-demo-named-entity
0
2
transformers
2023-05-16T16:13:13
--- language: - mn tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: mn-roberta-base-demo-named-entity results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mn-roberta-base-demo-named-entity This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1354 - Precision: 0.9239 - Recall: 0.9322 - F1: 0.9280 - Accuracy: 0.9797 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1651 | 1.0 | 477 | 0.0835 | 0.8900 | 0.9145 | 0.9021 | 0.9745 | | 0.0535 | 2.0 | 954 | 0.0780 | 0.9047 | 0.9243 | 0.9144 | 0.9775 | | 0.0267 | 3.0 | 1431 | 0.0836 | 0.9184 | 0.9307 | 0.9245 | 0.9790 | | 0.0159 | 4.0 | 1908 | 0.0936 | 0.9224 | 0.9329 | 0.9276 | 0.9803 | | 0.0083 | 5.0 | 2385 | 0.1155 | 0.9224 | 0.9307 | 0.9265 | 0.9790 | | 0.0055 | 6.0 | 2862 | 0.1211 | 0.9222 | 0.9316 | 0.9268 | 0.9793 | | 0.0034 | 7.0 | 3339 | 0.1258 | 0.9199 | 0.9329 | 0.9263 | 0.9789 | | 0.0025 | 8.0 | 3816 | 0.1300 | 0.9249 | 0.9339 | 0.9294 | 0.9799 | | 0.002 | 9.0 | 4293 | 0.1352 | 0.9231 | 0.9313 | 0.9272 | 0.9795 | | 0.0018 | 10.0 | 4770 | 0.1354 | 0.9239 | 0.9322 | 0.9280 | 0.9797 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
2,380
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land25/distilbert-base-uncased_emotion_ft_0416
2023-05-17T14:58:38.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
land25
null
null
land25/distilbert-base-uncased_emotion_ft_0416
0
2
transformers
2023-05-16T16:45:36
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 - precision model-index: - name: distilbert-base-uncased_emotion_ft_0416 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9375 - name: F1 type: f1 value: 0.9378516520466151 - name: Precision type: precision value: 0.9085326888984738 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_emotion_ft_0416 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1495 - Accuracy: 0.9375 - F1: 0.9379 - Precision: 0.9085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:| | 0.8285 | 1.0 | 250 | 0.2793 | 0.917 | 0.9150 | 0.9106 | | 0.2185 | 2.0 | 500 | 0.1718 | 0.926 | 0.9262 | 0.8978 | | 0.1413 | 3.0 | 750 | 0.1579 | 0.9325 | 0.9325 | 0.9096 | | 0.1147 | 4.0 | 1000 | 0.1495 | 0.9375 | 0.9379 | 0.9085 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
2,160
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asieh/bert-fine-tuned-cola
2023-05-22T14:08:55.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
asieh
null
null
asieh/bert-fine-tuned-cola
0
2
transformers
2023-05-16T17:18:54
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-fine-tuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5532122564572604 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8643 - Matthews Correlation: 0.5532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4782 | 1.0 | 1069 | 0.5697 | 0.4911 | | 0.3103 | 2.0 | 2138 | 0.6183 | 0.5820 | | 0.176 | 3.0 | 3207 | 0.8643 | 0.5532 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,840
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AustinCarthy/Baseline_20Kphish_benignFall_20_20_20
2023-05-17T15:52:06.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Baseline_20Kphish_benignFall_20_20_20
0
2
transformers
2023-05-16T19:07:03
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Baseline_20Kphish_benignFall_20_20_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Baseline_20Kphish_benignFall_20_20_20 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0540 - Accuracy: 0.9952 - F1: 0.9467 - Precision: 0.9984 - Recall: 0.9 - Roc Auc Score: 0.9500 - Tpr At Fpr 0.01: 0.9032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0065 | 1.0 | 13125 | 0.0309 | 0.991 | 0.8959 | 0.9975 | 0.813 | 0.9064 | 0.7808 | | 0.004 | 2.0 | 26250 | 0.0448 | 0.9926 | 0.9153 | 0.9988 | 0.8446 | 0.9223 | 0.8598 | | 0.0019 | 3.0 | 39375 | 0.0501 | 0.9938 | 0.9302 | 0.9986 | 0.8706 | 0.9353 | 0.8818 | | 0.0013 | 4.0 | 52500 | 0.0462 | 0.9954 | 0.9496 | 0.9967 | 0.9068 | 0.9533 | 0.895 | | 0.0 | 5.0 | 65625 | 0.0540 | 0.9952 | 0.9467 | 0.9984 | 0.9 | 0.9500 | 0.9032 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,233
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maxbarshay/Jordan_Name_Distinction
2023-05-22T14:19:29.000Z
[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
maxbarshay
null
null
maxbarshay/Jordan_Name_Distinction
0
2
transformers
2023-05-16T19:09:13
--- license: mit tags: - generated_from_trainer model-index: - name: Jordan_Name_Distinction results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Jordan_Name_Distinction This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - 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 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
1,025
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AustinCarthy/Baseline_30Kphish_benignFall_20_20_20
2023-05-17T16:04:45.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Baseline_30Kphish_benignFall_20_20_20
0
2
transformers
2023-05-16T20:35:36
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Baseline_30Kphish_benignFall_20_20_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Baseline_30Kphish_benignFall_20_20_20 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0374 - Accuracy: 0.9962 - F1: 0.9589 - Precision: 0.9998 - Recall: 0.9212 - Roc Auc Score: 0.9606 - Tpr At Fpr 0.01: 0.9438 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0045 | 1.0 | 19688 | 0.0304 | 0.9933 | 0.9241 | 0.9993 | 0.8594 | 0.9297 | 0.874 | | 0.0029 | 2.0 | 39376 | 0.0210 | 0.9967 | 0.9643 | 0.9953 | 0.9352 | 0.9675 | 0.917 | | 0.0003 | 3.0 | 59064 | 0.0434 | 0.9947 | 0.9407 | 0.9980 | 0.8896 | 0.9448 | 0.8936 | | 0.0016 | 4.0 | 78752 | 0.0408 | 0.9952 | 0.9468 | 0.9998 | 0.8992 | 0.9496 | 0.9336 | | 0.0008 | 5.0 | 98440 | 0.0374 | 0.9962 | 0.9589 | 0.9998 | 0.9212 | 0.9606 | 0.9438 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,236
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aalksii/albert-base-v2-ml-arxiv-papers
2023-06-01T12:45:05.000Z
[ "transformers", "pytorch", "tensorboard", "albert", "fill-mask", "en", "dataset:aalksii/ml-arxiv-papers", "dataset:CShorten/ML-ArXiv-Papers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
aalksii
null
null
aalksii/albert-base-v2-ml-arxiv-papers
0
2
transformers
2023-05-16T21:01:42
--- datasets: - aalksii/ml-arxiv-papers - CShorten/ML-ArXiv-Papers language: - en metrics: - perplexity pipeline_tag: fill-mask --- This model is a version of albert-base-v2, which is fine-tuned using MLM on ml-arxiv-papers dataset.
233
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MinaAlmasi/ES-ENG-mBERT-sentiment
2023-05-22T20:15:04.000Z
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
text-classification
MinaAlmasi
null
null
MinaAlmasi/ES-ENG-mBERT-sentiment
0
2
transformers
2023-05-16T21:24:54
--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: ES-ENG-mBERT-sentiment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ES-ENG-mBERT-sentiment This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on a Custom dataset. The best model (stopped after 14 epochs) achieves the following results on the evaluation set: - Loss: 0.8110 - Accuracy: 0.6307 - F1: 0.6298 - Precision: 0.6291 - Recall: 0.6307 ## Intended uses & limitations Note that commercial use with this model is prohibited. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.063 | 1.0 | 208 | 0.9989 | 0.4731 | 0.4044 | 0.4885 | 0.4731 | | 0.9664 | 2.0 | 416 | 0.9144 | 0.5262 | 0.4845 | 0.5270 | 0.5262 | | 0.9067 | 3.0 | 624 | 0.8648 | 0.5896 | 0.5844 | 0.5935 | 0.5896 | | 0.8572 | 4.0 | 832 | 0.8294 | 0.6065 | 0.5984 | 0.6102 | 0.6065 | | 0.8168 | 5.0 | 1040 | 0.8101 | 0.6107 | 0.6092 | 0.6119 | 0.6107 | | 0.7897 | 6.0 | 1248 | 0.8213 | 0.6074 | 0.6015 | 0.6018 | 0.6074 | | 0.7568 | 7.0 | 1456 | 0.7992 | 0.6194 | 0.6181 | 0.6176 | 0.6194 | | 0.7465 | 8.0 | 1664 | 0.8089 | 0.6246 | 0.6183 | 0.6206 | 0.6246 | | 0.7223 | 9.0 | 1872 | 0.7988 | 0.6236 | 0.6214 | 0.6207 | 0.6236 | | 0.7045 | 10.0 | 2080 | 0.8390 | 0.6165 | 0.6080 | 0.6126 | 0.6165 | | 0.6888 | 11.0 | 2288 | 0.8042 | 0.6291 | 0.6260 | 0.6257 | 0.6291 | | 0.671 | 12.0 | 2496 | 0.8088 | 0.6239 | 0.6212 | 0.6216 | 0.6239 | | 0.6543 | 13.0 | 2704 | 0.8104 | 0.6256 | 0.6227 | 0.6216 | 0.6256 | | 0.6409 | 14.0 | 2912 | 0.8110 | 0.6307 | 0.6298 | 0.6291 | 0.6307 | | 0.6275 | 15.0 | 3120 | 0.8127 | 0.6298 | 0.6292 | 0.6299 | 0.6298 | | 0.6176 | 16.0 | 3328 | 0.8334 | 0.6252 | 0.6217 | 0.6206 | 0.6252 | | 0.6096 | 17.0 | 3536 | 0.8331 | 0.6256 | 0.6210 | 0.6210 | 0.6256 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
2,964
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aalksii/distilbert-base-uncased-ml-arxiv-papers
2023-06-01T12:44:33.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "en", "dataset:aalksii/ml-arxiv-papers", "dataset:CShorten/ML-ArXiv-Papers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
aalksii
null
null
aalksii/distilbert-base-uncased-ml-arxiv-papers
0
2
transformers
2023-05-16T22:04:22
--- language: - en metrics: - perplexity pipeline_tag: fill-mask datasets: - aalksii/ml-arxiv-papers - CShorten/ML-ArXiv-Papers --- This model is a version of distilbert-base-uncased, which is fine-tuned using MLM on ml-arxiv-papers dataset.
242
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AustinCarthy/Baseline_40Kphish_benignFall_20_20_20
2023-05-17T16:17:16.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Baseline_40Kphish_benignFall_20_20_20
0
2
transformers
2023-05-16T22:42:42
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Baseline_40Kphish_benignFall_20_20_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Baseline_40Kphish_benignFall_20_20_20 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0374 - Accuracy: 0.9958 - F1: 0.9536 - Precision: 0.9985 - Recall: 0.9126 - Roc Auc Score: 0.9563 - Tpr At Fpr 0.01: 0.9268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0045 | 1.0 | 26250 | 0.0211 | 0.9949 | 0.9441 | 0.9962 | 0.8972 | 0.9485 | 0.8784 | | 0.0018 | 2.0 | 52500 | 0.0289 | 0.9957 | 0.9528 | 0.9967 | 0.9126 | 0.9562 | 0.9002 | | 0.0021 | 3.0 | 78750 | 0.0317 | 0.9940 | 0.9325 | 0.9993 | 0.874 | 0.9370 | 0.9172 | | 0.0014 | 4.0 | 105000 | 0.0315 | 0.9955 | 0.9504 | 0.9976 | 0.9074 | 0.9536 | 0.9046 | | 0.0003 | 5.0 | 131250 | 0.0374 | 0.9958 | 0.9536 | 0.9985 | 0.9126 | 0.9563 | 0.9268 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,243
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futuredatascience/welcome_video_model
2023-05-16T22:51:17.000Z
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:futuredatascience/autotrain-data-welcome_message_2", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
text-classification
futuredatascience
null
null
futuredatascience/welcome_video_model
0
2
transformers
2023-05-16T22:49:40
--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - futuredatascience/autotrain-data-welcome_message_2 co2_eq_emissions: emissions: 0.5524527127969758 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 59180133582 - CO2 Emissions (in grams): 0.5525 ## Validation Metrics - Loss: 0.347 - Accuracy: 0.865 - Precision: 0.852 - Recall: 0.958 - AUC: 0.814 - F1: 0.902 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/futuredatascience/autotrain-welcome_message_2-59180133582 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("futuredatascience/autotrain-welcome_message_2-59180133582", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("futuredatascience/autotrain-welcome_message_2-59180133582", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
1,202
[ [ -0.0269012451171875, -0.025299072265625, 0.01470184326171875, 0.00861358642578125, -0.0028743743896484375, -0.0006165504455566406, 0.006931304931640625, -0.019683837890625, 0.004566192626953125, 0.011749267578125, -0.06024169921875, -0.0341796875, -0.05911254882...
mtreviso/roberta-base-snli
2023-05-17T00:27:28.000Z
[ "transformers", "pytorch", "jax", "roberta", "text-classification", "endpoints_compatible", "region:us" ]
text-classification
mtreviso
null
null
mtreviso/roberta-base-snli
0
2
transformers
2023-05-17T00:27:04
--- duplicated_from: boychaboy/SNLI_roberta-base --- Forked from: https://huggingface.co/boychaboy/SNLI_roberta-base
117
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lgfunderburk/distilbert-truncated
2023-05-17T02:52:29.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
lgfunderburk
null
null
lgfunderburk/distilbert-truncated
0
2
transformers
2023-05-17T00:42:05
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert-truncated results: [] --- # distilbert-truncated This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [20 Newsgroups dataset](http://qwone.com/~jason/20Newsgroups/). It achieves the following results on the evaluation set: ## Training and evaluation data The data was split into training and testing: model trained on 90% of the data, and had a testing data size of 10% of the original dataset. ## Training procedure DistilBERT has a maximum input length of 512, so with this in mind the following was performed: 1. I used the `distilbert-base-uncased` pretrained model to initialize an `AutoTokenizer`. 2. Setting a maximum length of 256, each entry in the training, testing and validation data was truncated if it exceeded the limit and padded if it didn't reach the limit. ### 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': 1908, '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 ### Training results EPOCHS = 3 batches_per_epoch = 636 total_train_steps = 1908 Model accuracy 0.8337758779525757 Model loss 0.568471074104309 ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,811
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denaneek/building-with-llms
2023-05-17T19:17:05.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
denaneek
null
null
denaneek/building-with-llms
0
2
transformers
2023-05-17T00:48:58
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: building-with-llms results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # building-with-llms 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,443
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AustinCarthy/Baseline_50Kphish_benignFall_20_20_20
2023-05-17T16:29:51.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Baseline_50Kphish_benignFall_20_20_20
0
2
transformers
2023-05-17T01:28:03
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Baseline_50Kphish_benignFall_20_20_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Baseline_50Kphish_benignFall_20_20_20 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0282 - Accuracy: 0.9962 - F1: 0.9580 - Precision: 0.9996 - Recall: 0.9198 - Roc Auc Score: 0.9599 - Tpr At Fpr 0.01: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0045 | 1.0 | 32813 | 0.0247 | 0.9960 | 0.9561 | 0.9937 | 0.9212 | 0.9605 | 0.8662 | | 0.002 | 2.0 | 65626 | 0.0205 | 0.9965 | 0.9624 | 0.9987 | 0.9286 | 0.9643 | 0.9376 | | 0.0021 | 3.0 | 98439 | 0.0302 | 0.9961 | 0.9569 | 0.9993 | 0.918 | 0.9590 | 0.9378 | | 0.0017 | 4.0 | 131252 | 0.0297 | 0.9970 | 0.9672 | 0.9975 | 0.9388 | 0.9693 | 0.9368 | | 0.0007 | 5.0 | 164065 | 0.0282 | 0.9962 | 0.9580 | 0.9996 | 0.9198 | 0.9599 | 0.94 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,241
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vocabtrimmer/roberta-base-xnli-en
2023-05-17T02:40:18.000Z
[ "transformers", "pytorch", "roberta", "text-classification", "endpoints_compatible", "region:us" ]
text-classification
vocabtrimmer
null
null
vocabtrimmer/roberta-base-xnli-en
0
2
transformers
2023-05-17T02:38:56
# `vocabtrimmer/roberta-base-xnli-en` This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [xnli](https://huggingface.co/datasets/xnli) (en). Following metrics are computed on the `test` split of [xnli](https://huggingface.co/datasets/xnli)(en). | | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy | |---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:| | 0 | 87.54 | 87.54 | 87.54 | 87.6 | 87.54 | 87.86 | 87.54 | Check the result file [here](https://huggingface.co/vocabtrimmer/roberta-base-xnli-en/raw/main/eval.json).
867
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lferncastro/distilbert_classifier
2023-05-17T02:48:40.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
lferncastro
null
null
lferncastro/distilbert_classifier
0
2
transformers
2023-05-17T02:48:08
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,449
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WangCo/distilbert-base-uncased_emotion_ft_0416
2023-05-17T03:01:18.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
WangCo
null
null
WangCo/distilbert-base-uncased_emotion_ft_0416
0
2
transformers
2023-05-17T02:50:54
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased_emotion_ft_0416 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_emotion_ft_0416 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.11.0
1,079
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AustinCarthy/Benign10MGPT2_fromP_BFall_10KGen_toP_0.75
2023-05-17T16:43:29.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromP_BFall_10KGen_toP_0.75
0
2
transformers
2023-05-17T04:52:53
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromP_BFall_10KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromP_BFall_10KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1046 - Accuracy: 0.9898 - F1: 0.8806 - Precision: 0.9952 - Recall: 0.7896 - Roc Auc Score: 0.8947 - Tpr At Fpr 0.01: 0.7606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0104 | 1.0 | 13125 | 0.0568 | 0.9869 | 0.8415 | 0.9964 | 0.7282 | 0.8640 | 0.7054 | | 0.0078 | 2.0 | 26250 | 0.0722 | 0.9871 | 0.8440 | 0.9932 | 0.7338 | 0.8668 | 0.6516 | | 0.0047 | 3.0 | 39375 | 0.0675 | 0.9900 | 0.8833 | 0.9913 | 0.7966 | 0.8981 | 0.7312 | | 0.0011 | 4.0 | 52500 | 0.0811 | 0.9904 | 0.8888 | 0.9936 | 0.804 | 0.9019 | 0.7698 | | 0.0 | 5.0 | 65625 | 0.1046 | 0.9898 | 0.8806 | 0.9952 | 0.7896 | 0.8947 | 0.7606 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,244
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amqdn/distilbert-clf-20newsgroups
2023-05-17T05:28:23.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
amqdn
null
null
amqdn/distilbert-clf-20newsgroups
0
2
transformers
2023-05-17T05:16:21
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert-clf-20newsgroups results: [] --- # distilbert-clf-20newsgroups This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on 20newsgroups. It achieves the following results on the evaluation set: * loss: 0.5506 * accuracy: 0.8401 ## Model description ## Intended uses & limitations ## Training and evaluation data ## Training procedure ### 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': 1908, '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 ### Training results * loss: 0.2480 * accuracy: 0.9422 * val_loss: 0.3633 * val_accuracy: 0.8940 ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,307
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AustinCarthy/Benign10MGPT2_fromP_BFall_20KGen_toP_0.75
2023-05-17T16:56:10.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromP_BFall_20KGen_toP_0.75
0
2
transformers
2023-05-17T06:22:41
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromP_BFall_20KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromP_BFall_20KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1101 - Accuracy: 0.9888 - F1: 0.8669 - Precision: 0.9948 - Recall: 0.7682 - Roc Auc Score: 0.884 - Tpr At Fpr 0.01: 0.7442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0105 | 1.0 | 19688 | 0.0686 | 0.9851 | 0.8158 | 0.9957 | 0.691 | 0.8454 | 0.654 | | 0.0069 | 2.0 | 39376 | 0.0458 | 0.9901 | 0.8866 | 0.9794 | 0.8098 | 0.9045 | 0.679 | | 0.0051 | 3.0 | 59064 | 0.0698 | 0.9903 | 0.8874 | 0.9901 | 0.804 | 0.9018 | 0.747 | | 0.0013 | 4.0 | 78752 | 0.0980 | 0.9893 | 0.8737 | 0.9949 | 0.7788 | 0.8893 | 0.7374 | | 0.0007 | 5.0 | 98440 | 0.1101 | 0.9888 | 0.8669 | 0.9948 | 0.7682 | 0.884 | 0.7442 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,243
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bhattronak14/distilbert-base-uncased-finetuned-rte
2023-05-18T10:55:03.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
bhattronak14
null
null
bhattronak14/distilbert-base-uncased-finetuned-rte
0
2
transformers
2023-05-17T06:31:18
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-rte results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-rte This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3
1,041
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vnktrmnb/fine_tune_bert_output_te_ner
2023-05-17T06:48:09.000Z
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
vnktrmnb
null
null
vnktrmnb/fine_tune_bert_output_te_ner
0
2
transformers
2023-05-17T06:46:40
--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann model-index: - name: fine_tune_bert_output_te_ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine_tune_bert_output_te_ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,096
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AustinCarthy/Benign10MGPT2_fromP_BFall_30KGen_toP_0.75
2023-05-17T17:08:54.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromP_BFall_30KGen_toP_0.75
0
2
transformers
2023-05-17T08:31:17
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromP_BFall_30KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromP_BFall_30KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0981 - Accuracy: 0.9876 - F1: 0.8504 - Precision: 0.9938 - Recall: 0.7432 - Roc Auc Score: 0.8715 - Tpr At Fpr 0.01: 0.6914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0097 | 1.0 | 26250 | 0.0808 | 0.9840 | 0.8004 | 0.9874 | 0.673 | 0.8363 | 0.6018 | | 0.011 | 2.0 | 52500 | 0.0652 | 0.9867 | 0.8389 | 0.9881 | 0.7288 | 0.8642 | 0.6536 | | 0.0025 | 3.0 | 78750 | 0.0730 | 0.9868 | 0.8401 | 0.9889 | 0.7302 | 0.8649 | 0.649 | | 0.0023 | 4.0 | 105000 | 0.1064 | 0.9866 | 0.8367 | 0.9937 | 0.7226 | 0.8612 | 0.6878 | | 0.0011 | 5.0 | 131250 | 0.0981 | 0.9876 | 0.8504 | 0.9938 | 0.7432 | 0.8715 | 0.6914 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,251
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AustinCarthy/Benign10MGPT2_fromP_BFall_40KGen_toP_0.75
2023-05-17T17:21:27.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromP_BFall_40KGen_toP_0.75
0
2
transformers
2023-05-17T11:17:57
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromP_BFall_40KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromP_BFall_40KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0969 - Accuracy: 0.9891 - F1: 0.8714 - Precision: 0.9941 - Recall: 0.7756 - Roc Auc Score: 0.8877 - Tpr At Fpr 0.01: 0.7466 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0125 | 1.0 | 32813 | 0.0595 | 0.9879 | 0.8582 | 0.9722 | 0.7682 | 0.8836 | 0.4626 | | 0.0073 | 2.0 | 65626 | 0.0586 | 0.9881 | 0.8574 | 0.9934 | 0.7542 | 0.8770 | 0.7238 | | 0.0057 | 3.0 | 98439 | 0.0760 | 0.987 | 0.8426 | 0.9948 | 0.7308 | 0.8653 | 0.7106 | | 0.0028 | 4.0 | 131252 | 0.0734 | 0.9896 | 0.8778 | 0.9937 | 0.7862 | 0.8930 | 0.7676 | | 0.0013 | 5.0 | 164065 | 0.0969 | 0.9891 | 0.8714 | 0.9941 | 0.7756 | 0.8877 | 0.7466 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,251
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soteroshanthi/distilbert-base-uncased
2023-05-17T12:24:33.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
soteroshanthi
null
null
soteroshanthi/distilbert-base-uncased
0
2
transformers
2023-05-17T12:24:21
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert-base-uncased results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,453
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bastienm/dqn-SpaceInvadersNoFrameskip-v4
2023-05-17T12:53:34.000Z
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
bastienm
null
null
bastienm/dqn-SpaceInvadersNoFrameskip-v4
0
2
stable-baselines3
2023-05-17T12:52:59
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 552.00 +/- 166.57 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bastienm -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bastienm -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bastienm ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
2,691
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Gaivoronsky/dqn-SpaceInvadersNoFrameskip-v4
2023-05-17T14:00:26.000Z
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
Gaivoronsky
null
null
Gaivoronsky/dqn-SpaceInvadersNoFrameskip-v4
0
2
stable-baselines3
2023-05-17T13:59:54
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 559.50 +/- 161.98 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Gaivoronsky -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Gaivoronsky -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Gaivoronsky ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
2,698
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chenbowen-184/distilbert_classifier_newsgroups
2023-05-17T14:22:57.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
chenbowen-184
null
null
chenbowen-184/distilbert_classifier_newsgroups
0
2
transformers
2023-05-17T14:22:25
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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TasmiaAzmi/masked-sentence-generation-t5-base
2023-05-19T06:33:53.000Z
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
TasmiaAzmi
null
null
TasmiaAzmi/masked-sentence-generation-t5-base
0
2
transformers
2023-05-17T15:56:02
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: masked-sentence-generation-t5-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # masked-sentence-generation-t5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9984 | 0.05 | 80 | 2.7041 | | 2.8752 | 0.1 | 160 | 2.7021 | | 2.9314 | 0.15 | 240 | 2.6966 | | 2.8541 | 0.2 | 320 | 2.6968 | | 2.8674 | 0.25 | 400 | 2.6900 | | 2.8706 | 0.3 | 480 | 2.6886 | | 2.7718 | 0.34 | 560 | 2.6908 | | 2.8503 | 0.39 | 640 | 2.6877 | | 2.8195 | 0.44 | 720 | 2.6902 | | 2.8569 | 0.49 | 800 | 2.6893 | | 2.8372 | 0.54 | 880 | 2.6859 | | 2.8915 | 0.59 | 960 | 2.6898 | | 2.9687 | 0.64 | 1040 | 2.6909 | | 2.832 | 0.69 | 1120 | 2.6841 | | 2.8425 | 0.74 | 1200 | 2.6842 | | 2.8114 | 0.79 | 1280 | 2.6766 | | 2.8101 | 0.84 | 1360 | 2.6783 | | 2.8837 | 0.89 | 1440 | 2.6781 | | 2.894 | 0.94 | 1520 | 2.6754 | | 2.9183 | 0.99 | 1600 | 2.6762 | | 2.6916 | 1.03 | 1680 | 2.6889 | | 2.5812 | 1.08 | 1760 | 2.6896 | | 2.5522 | 1.13 | 1840 | 2.6943 | | 2.5368 | 1.18 | 1920 | 2.6928 | | 2.5987 | 1.23 | 2000 | 2.6927 | | 2.5625 | 1.28 | 2080 | 2.6899 | | 2.4946 | 1.33 | 2160 | 2.6942 | | 2.5902 | 1.38 | 2240 | 2.6900 | | 2.5415 | 1.43 | 2320 | 2.6897 | | 2.5767 | 1.48 | 2400 | 2.6858 | | 2.6262 | 1.53 | 2480 | 2.6825 | | 2.6066 | 1.58 | 2560 | 2.6818 | | 2.5387 | 1.63 | 2640 | 2.6840 | | 2.5795 | 1.67 | 2720 | 2.6828 | | 2.5521 | 1.72 | 2800 | 2.6871 | | 2.5477 | 1.77 | 2880 | 2.6836 | | 2.587 | 1.82 | 2960 | 2.6824 | | 2.529 | 1.87 | 3040 | 2.6871 | | 2.6221 | 1.92 | 3120 | 2.6838 | | 2.6353 | 1.97 | 3200 | 2.6803 | | 2.5419 | 2.02 | 3280 | 2.6879 | | 2.4521 | 2.07 | 3360 | 2.7027 | | 2.3415 | 2.12 | 3440 | 2.7105 | | 2.3483 | 2.17 | 3520 | 2.7140 | | 2.3493 | 2.22 | 3600 | 2.7144 | | 2.3967 | 2.27 | 3680 | 2.7134 | | 2.3544 | 2.32 | 3760 | 2.7122 | | 2.3192 | 2.36 | 3840 | 2.7175 | | 2.3381 | 2.41 | 3920 | 2.7166 | | 2.3667 | 2.46 | 4000 | 2.7165 | | 2.3997 | 2.51 | 4080 | 2.7106 | | 2.3178 | 2.56 | 4160 | 2.7154 | | 2.4036 | 2.61 | 4240 | 2.7144 | | 2.3797 | 2.66 | 4320 | 2.7129 | | 2.3354 | 2.71 | 4400 | 2.7136 | | 2.4109 | 2.76 | 4480 | 2.7118 | | 2.387 | 2.81 | 4560 | 2.7097 | | 2.3934 | 2.86 | 4640 | 2.7103 | | 2.3956 | 2.91 | 4720 | 2.7103 | | 2.4086 | 2.96 | 4800 | 2.7111 | | 2.4083 | 3.0 | 4880 | 2.7110 | | 2.3121 | 3.05 | 4960 | 2.7230 | | 2.263 | 3.1 | 5040 | 2.7252 | | 2.2722 | 3.15 | 5120 | 2.7296 | | 2.2053 | 3.2 | 5200 | 2.7309 | | 2.1969 | 3.25 | 5280 | 2.7363 | | 2.2684 | 3.3 | 5360 | 2.7396 | | 2.2789 | 3.35 | 5440 | 2.7376 | | 2.2227 | 3.4 | 5520 | 2.7384 | | 2.2886 | 3.45 | 5600 | 2.7390 | | 2.2182 | 3.5 | 5680 | 2.7376 | | 2.2738 | 3.55 | 5760 | 2.7394 | | 2.1687 | 3.6 | 5840 | 2.7386 | | 2.2548 | 3.65 | 5920 | 2.7371 | | 2.2391 | 3.69 | 6000 | 2.7372 | | 2.2031 | 3.74 | 6080 | 2.7391 | | 2.1885 | 3.79 | 6160 | 2.7400 | | 2.216 | 3.84 | 6240 | 2.7406 | | 2.272 | 3.89 | 6320 | 2.7401 | | 2.3455 | 3.94 | 6400 | 2.7395 | | 2.2889 | 3.99 | 6480 | 2.7392 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.11.0
5,341
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Ankit93/distilbert-base-uncased-finetuned-emotion
2023-05-18T19:30:37.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
Ankit93
null
null
Ankit93/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-17T15:57:25
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9284458409041368 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2192 - Accuracy: 0.9285 - F1: 0.9284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8301 | 1.0 | 250 | 0.3214 | 0.905 | 0.9010 | | 0.2508 | 2.0 | 500 | 0.2192 | 0.9285 | 0.9284 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
1,848
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land25/distilbert-base-uncased_emotion_ft_0517
2023-05-17T16:26:03.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
land25
null
null
land25/distilbert-base-uncased_emotion_ft_0517
0
2
transformers
2023-05-17T16:04:08
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 - precision model-index: - name: distilbert-base-uncased_emotion_ft_0517 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9345 - name: F1 type: f1 value: 0.9346851141275695 - name: Precision type: precision value: 0.9087842847016905 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_emotion_ft_0517 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1479 - Accuracy: 0.9345 - F1: 0.9347 - Precision: 0.9088 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:| | 0.7913 | 1.0 | 250 | 0.2689 | 0.918 | 0.9162 | 0.9016 | | 0.2142 | 2.0 | 500 | 0.1764 | 0.929 | 0.9290 | 0.9109 | | 0.1415 | 3.0 | 750 | 0.1541 | 0.934 | 0.9345 | 0.8995 | | 0.1128 | 4.0 | 1000 | 0.1479 | 0.9345 | 0.9347 | 0.9088 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
2,166
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cmagganas/distilbert_classifier_newsgroups
2023-05-17T16:39:08.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
cmagganas
null
null
cmagganas/distilbert_classifier_newsgroups
0
2
transformers
2023-05-17T16:36:38
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results Achieved 83.4% acc. ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,490
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estevez-work/distilbert_classifier_newsgroups
2023-05-17T18:31:45.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
estevez-work
null
null
estevez-work/distilbert_classifier_newsgroups
0
2
transformers
2023-05-17T18:31:11
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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AustinCarthy/Benign10MGPT2_fromB_BFall_10KGen_toP_0.75
2023-05-17T20:50:18.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromB_BFall_10KGen_toP_0.75
0
2
transformers
2023-05-17T19:06:42
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromB_BFall_10KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromB_BFall_10KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0932 - Accuracy: 0.9863 - F1: 0.8426 - Precision: 0.9285 - Recall: 0.7712 - Roc Auc Score: 0.8841 - Tpr At Fpr 0.01: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0731 | 1.0 | 13125 | 0.0701 | 0.9834 | 0.8069 | 0.9013 | 0.7304 | 0.8632 | 0.5672 | | 0.0595 | 2.0 | 26250 | 0.0720 | 0.9812 | 0.7700 | 0.9192 | 0.6624 | 0.8297 | 0.5038 | | 0.0457 | 3.0 | 39375 | 0.0667 | 0.9864 | 0.8459 | 0.9193 | 0.7834 | 0.8900 | 0.0 | | 0.0301 | 4.0 | 52500 | 0.0803 | 0.9861 | 0.8368 | 0.9467 | 0.7498 | 0.8738 | 0.0 | | 0.02 | 5.0 | 65625 | 0.0932 | 0.9863 | 0.8426 | 0.9285 | 0.7712 | 0.8841 | 0.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,241
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everyl12/user_class_L
2023-05-17T20:58:34.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
everyl12
null
null
everyl12/user_class_L
0
2
transformers
2023-05-17T20:49:10
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: user_class_L results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # user_class_L This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5451 - Accuracy: 0.9237 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.8e-05 - train_batch_size: 30 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1572 | 1.0 | 24 | 0.2433 | 0.9025 | | 0.1649 | 2.0 | 48 | 0.2262 | 0.9237 | | 0.2498 | 3.0 | 72 | 0.2584 | 0.9237 | | 0.006 | 4.0 | 96 | 0.3393 | 0.9153 | | 0.0035 | 5.0 | 120 | 0.3967 | 0.9153 | | 0.0017 | 6.0 | 144 | 0.4777 | 0.9153 | | 0.0006 | 7.0 | 168 | 0.6257 | 0.8898 | | 0.0005 | 8.0 | 192 | 0.5752 | 0.9153 | | 0.0002 | 9.0 | 216 | 0.5182 | 0.9237 | | 0.0003 | 10.0 | 240 | 0.5041 | 0.9195 | | 0.0002 | 11.0 | 264 | 0.5051 | 0.9195 | | 0.0001 | 12.0 | 288 | 0.5292 | 0.9195 | | 0.0002 | 13.0 | 312 | 0.5391 | 0.9237 | | 0.0002 | 14.0 | 336 | 0.5437 | 0.9237 | | 0.0002 | 15.0 | 360 | 0.5451 | 0.9237 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.2
2,185
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AustinCarthy/Benign10MGPT2_fromB_BFall_20KGen_toP_0.75
2023-05-18T02:40:30.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromB_BFall_20KGen_toP_0.75
0
2
transformers
2023-05-17T20:51:32
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromB_BFall_20KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromB_BFall_20KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1022 - Accuracy: 0.9840 - F1: 0.8164 - Precision: 0.8982 - Recall: 0.7482 - Roc Auc Score: 0.8720 - Tpr At Fpr 0.01: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0758 | 1.0 | 19688 | 0.0958 | 0.9786 | 0.7257 | 0.9311 | 0.5946 | 0.7962 | 0.5118 | | 0.0634 | 2.0 | 39376 | 0.0682 | 0.9823 | 0.7843 | 0.9367 | 0.6746 | 0.8362 | 0.4936 | | 0.0515 | 3.0 | 59064 | 0.0760 | 0.9823 | 0.7955 | 0.8855 | 0.7222 | 0.8588 | 0.6002 | | 0.0372 | 4.0 | 78752 | 0.0951 | 0.9831 | 0.8034 | 0.8979 | 0.7268 | 0.8613 | 0.0 | | 0.0339 | 5.0 | 98440 | 0.1022 | 0.9840 | 0.8164 | 0.8982 | 0.7482 | 0.8720 | 0.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,241
[ [ -0.04351806640625, -0.04364013671875, 0.0081787109375, 0.0094146728515625, -0.0219573974609375, -0.0241851806640625, -0.007724761962890625, -0.0199127197265625, 0.02691650390625, 0.0247650146484375, -0.050689697265625, -0.046356201171875, -0.052703857421875, ...
cs608/billsum-full-data
2023-05-18T00:06:56.000Z
[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
cs608
null
null
cs608/billsum-full-data
0
2
transformers
2023-05-17T21:02:45
--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: billsum-full-data results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: train[:95%] args: default metrics: - name: Rouge1 type: rouge value: 18.0383 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # billsum-full-data This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 1.6583 - Rouge1: 18.0383 - Rouge2: 14.8462 - Rougel: 17.6086 - Rougelsum: 17.6843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.1401 | 1.0 | 8101 | 1.8087 | 17.8461 | 14.6015 | 17.3956 | 17.4842 | | 1.7596 | 2.0 | 16202 | 1.6980 | 18.0568 | 14.7833 | 17.6068 | 17.6999 | | 1.5789 | 3.0 | 24303 | 1.6583 | 18.0383 | 14.8462 | 17.6086 | 17.6843 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,978
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christinacdl/XLM_Roberta_Large_Greek_Offensive
2023-05-20T10:15:09.000Z
[ "transformers", "pytorch", "tensorboard", "xlm-roberta-xl", "text-classification", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
text-classification
christinacdl
null
null
christinacdl/XLM_Roberta_Large_Greek_Offensive
0
2
transformers
2023-05-17T21:10:57
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: XLM_Roberta_Large_Greek_Offensive results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # XLM_Roberta_Large_Greek_Offensive This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7552 - Macro F1: 0.7352 - Micro F1: 0.7989 - Accuracy: 0.7989 Results on test set: -Accuracy: 0.905440414507772 -F1 score: 0.8394228385651885 -Precision: 0.8115009990009989 -Recall : 0.8800129489279049 -Matthews Correlation Coefficient: 0.6881116572893037 -Precision of each class: [0.96915584 0.65384615] -Recall of each class: [0.91705069 0.84297521] -F1 score of each class: [0.94238358 0.73646209] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:| | 0.547 | 1.0 | 1967 | 0.6330 | 0.7245 | 0.8057 | 0.8057 | | 0.5369 | 2.0 | 3934 | 0.5186 | 0.7328 | 0.8057 | 0.8057 | | 0.5571 | 3.0 | 5901 | 0.6156 | 0.7495 | 0.8149 | 0.8149 | | 0.5426 | 4.0 | 7868 | 0.6820 | 0.7388 | 0.8126 | 0.8126 | | 0.4842 | 5.0 | 9835 | 0.7268 | 0.7386 | 0.7897 | 0.7897 | | 0.5113 | 6.0 | 11802 | 0.7552 | 0.7352 | 0.7989 | 0.7989 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.1+cu118 - Datasets 2.9.0 - Tokenizers 0.13.3
2,332
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modelscope-unofficial/damo-csanmt-zh-en-large-tfs
2023-05-18T19:51:07.000Z
[ "keras", "translation", "license:apache-2.0", "region:us" ]
translation
modelscope-unofficial
null
null
modelscope-unofficial/damo-csanmt-zh-en-large-tfs
0
2
keras
2023-05-17T23:29:46
--- license: apache-2.0 pipeline_tag: translation --- TensorFlow saved model version of the original model: https://www.modelscope.cn/models/damo/nlp_csanmt_translation_zh2en/summary
183
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pkuong/distilbert_classifier_newsgroups
2023-05-18T00:22:29.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
pkuong
null
null
pkuong/distilbert_classifier_newsgroups
0
2
transformers
2023-05-18T00:22:11
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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yarak001/distilbert-base-uncased-finetuned-emotion
2023-05-18T01:03:56.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
yarak001
null
null
yarak001/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-18T00:28:41
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.9225635095680048 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2207 - Accuracy: 0.9225 - F1: 0.9226 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8134 | 1.0 | 250 | 0.3127 | 0.903 | 0.9000 | | 0.247 | 2.0 | 500 | 0.2207 | 0.9225 | 0.9226 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,848
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korelidw/bert_simple_classifier
2023-05-18T02:25:27.000Z
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
korelidw
null
null
korelidw/bert_simple_classifier
0
2
transformers
2023-05-18T02:24:39
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert_simple_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bert_simple_classifier 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 3054, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,439
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AustinCarthy/Benign10MGPT2_fromB_BFall_30KGen_toP_0.75
2023-05-18T05:44:42.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromB_BFall_30KGen_toP_0.75
0
2
transformers
2023-05-18T02:42:03
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromB_BFall_30KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromB_BFall_30KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1066 - Accuracy: 0.9827 - F1: 0.7997 - Precision: 0.8920 - Recall: 0.7248 - Roc Auc Score: 0.8602 - Tpr At Fpr 0.01: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0859 | 1.0 | 26250 | 0.0749 | 0.9823 | 0.7832 | 0.9388 | 0.6718 | 0.8348 | 0.5556 | | 0.074 | 2.0 | 52500 | 0.0810 | 0.9803 | 0.7718 | 0.8628 | 0.6982 | 0.8463 | 0.5496 | | 0.0534 | 3.0 | 78750 | 0.0735 | 0.9846 | 0.8211 | 0.9211 | 0.7406 | 0.8687 | 0.5882 | | 0.0374 | 4.0 | 105000 | 0.0877 | 0.9830 | 0.8023 | 0.8976 | 0.7254 | 0.8606 | 0.0 | | 0.0267 | 5.0 | 131250 | 0.1066 | 0.9827 | 0.7997 | 0.8920 | 0.7248 | 0.8602 | 0.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,248
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SHENMU007/neunit_testv1.1
2023-05-18T05:51:56.000Z
[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]
text-to-audio
SHENMU007
null
null
SHENMU007/neunit_testv1.1
0
2
transformers
2023-05-18T03:36:40
--- language: - zh license: mit tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.12.1
1,251
[ [ -0.0350341796875, -0.051727294921875, -0.005931854248046875, 0.01265716552734375, -0.025390625, -0.0193939208984375, -0.01763916015625, -0.0265045166015625, 0.0114288330078125, 0.021270751953125, -0.0411376953125, -0.050048828125, -0.04315185546875, 0.008583...
moghis/ppo-Pyramids
2023-05-18T04:53:06.000Z
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
moghis
null
null
moghis/ppo-Pyramids
0
2
ml-agents
2023-05-18T04:53:01
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: moghis/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
949
[ [ -0.02734375, -0.01934814453125, -0.0010852813720703125, 0.0255889892578125, -0.0098114013671875, 0.005950927734375, 0.027740478515625, -0.0028057098388671875, 0.0355224609375, 0.035247802734375, -0.035858154296875, -0.052001953125, -0.035369873046875, -0.010...
fredymad/distilbert_Pfinal_2e-5_16_2
2023-06-02T10:35:51.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "endpoints_compatible", "region:us" ]
text-classification
fredymad
null
null
fredymad/distilbert_Pfinal_2e-5_16_2
0
2
transformers
2023-05-18T04:59:25
--- tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert_Pfinal_2e-5_16_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_Pfinal_2e-5_16_2 This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2200 - F1: 0.7289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2427 | 1.0 | 669 | 0.1984 | 0.7270 | | 0.1799 | 2.0 | 1338 | 0.2200 | 0.7289 | ### Framework versions - Transformers 4.28.0 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
1,417
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atrytone/scibert_uncased_claim_id
2023-06-17T15:16:47.000Z
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
atrytone
null
null
atrytone/scibert_uncased_claim_id
0
2
transformers
2023-05-18T05:07:15
--- license: apache-2.0 language: - en --- Fine-tuned SciBERT uncased model [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for claim detection from abstracts.
204
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suraj47K/keras-dummy-sequential
2023-05-18T05:42:09.000Z
[ "keras", "region:us" ]
null
suraj47K
null
null
suraj47K/keras-dummy-sequential
0
2
keras
2023-05-18T05:42:07
--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: | Hyperparameters | Value | | :-- | :-- | | 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 | False | | is_legacy_optimizer | False | | learning_rate | 0.0010000000474974513 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
841
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AustinCarthy/Benign10MGPT2_fromB_BFall_40KGen_toP_0.75
2023-05-18T09:25:30.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Benign10MGPT2_fromB_BFall_40KGen_toP_0.75
0
2
transformers
2023-05-18T05:44:57
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Benign10MGPT2_fromB_BFall_40KGen_toP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Benign10MGPT2_fromB_BFall_40KGen_toP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1061 - Accuracy: 0.9824 - F1: 0.7918 - Precision: 0.9034 - Recall: 0.7048 - Roc Auc Score: 0.8505 - Tpr At Fpr 0.01: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0873 | 1.0 | 32813 | 0.0943 | 0.9790 | 0.7389 | 0.9064 | 0.6236 | 0.8102 | 0.19 | | 0.0715 | 2.0 | 65626 | 0.0807 | 0.9817 | 0.7803 | 0.9099 | 0.683 | 0.8398 | 0.4716 | | 0.0501 | 3.0 | 98439 | 0.0727 | 0.9834 | 0.8103 | 0.8917 | 0.7426 | 0.8690 | 0.0 | | 0.0436 | 4.0 | 131252 | 0.0833 | 0.9843 | 0.8217 | 0.8976 | 0.7576 | 0.8766 | 0.0 | | 0.0292 | 5.0 | 164065 | 0.1061 | 0.9824 | 0.7918 | 0.9034 | 0.7048 | 0.8505 | 0.0 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,248
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ashleyradford/my_awesome_food_model
2023-05-18T21:04:32.000Z
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "en", "dataset:food101", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
ashleyradford
null
null
ashleyradford/my_awesome_food_model
0
2
transformers
2023-05-18T06:15:19
--- datasets: - food101 language: - en metrics: - accuracy library_name: transformers --- # Image Classification Classifies food images using a subset of the food101 dataset.<br> Uses PyTorch for the preprocessing, training, and inference. ``` output_dir="cats_vs_dogs_model" remove_unused_columns=False evaluation_strategy="epoch" save_strategy="epoch" learning_rate=5e-5 per_device_train_batch_size=16 gradient_accumulation_steps=4 per_device_eval_batch_size=16 num_train_epochs=3 warmup_ratio=0.1 logging_steps=10 load_best_model_at_end=True metric_for_best_model="accuracy" push_to_hub=True ```
629
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SHENMU007/neunit_tts_1.0
2023-05-18T07:58:28.000Z
[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]
text-to-audio
SHENMU007
null
null
SHENMU007/neunit_tts_1.0
0
2
transformers
2023-05-18T06:15:59
--- language: - zh license: mit tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.12.1
1,251
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gkrishnan/distilbert_classifier_newsgroups
2023-05-18T06:39:35.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
gkrishnan
null
null
gkrishnan/distilbert_classifier_newsgroups
0
2
transformers
2023-05-18T06:39:03
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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againeureka/imdb_binary_classifier_roberta_base
2023-06-20T07:47:37.000Z
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "license:mit", "model-index", "endpoints_compatible", "region:us" ]
text-classification
againeureka
null
null
againeureka/imdb_binary_classifier_roberta_base
0
2
transformers
2023-05-18T07:00:29
--- license: mit tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: imdb_binary_classifier_roberta_base results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9538 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # imdb_binary_classifier_roberta_base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2530 - Accuracy: 0.9538 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2575 | 1.0 | 782 | 0.1513 | 0.9461 | | 0.1272 | 2.0 | 1564 | 0.1784 | 0.9482 | | 0.0859 | 3.0 | 2346 | 0.1854 | 0.9510 | | 0.0506 | 4.0 | 3128 | 0.2193 | 0.9529 | | 0.0341 | 5.0 | 3910 | 0.2530 | 0.9538 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.2
1,869
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vnsaipa1/t5-small-finetuned
2023-05-18T07:09:26.000Z
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
vnsaipa1
null
null
vnsaipa1/t5-small-finetuned
0
2
transformers
2023-05-18T07:07:33
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
991
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bortle/moon-detector-v5.a
2023-05-18T09:22:43.000Z
[ "transformers", "pytorch", "vit", "image-classification", "vision", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
image-classification
bortle
null
null
bortle/moon-detector-v5.a
0
2
transformers
2023-05-18T07:34:54
--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: moon-detector-v5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9949622166246851 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # moon-detector-v5 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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0238 - Accuracy: 0.9950 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0548 | 1.0 | 281 | 0.0616 | 0.9798 | | 0.1366 | 2.0 | 562 | 0.0340 | 0.9899 | | 0.0218 | 3.0 | 843 | 0.0430 | 0.9874 | | 0.0403 | 4.0 | 1124 | 0.0406 | 0.9874 | | 0.0184 | 5.0 | 1405 | 0.0238 | 0.9950 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.3
1,954
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Zamill/distilbert-base-uncased-finetuned-emotion
2023-05-18T08:06:05.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
Zamill
null
null
Zamill/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-18T08:01:54
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.921 - name: F1 type: f1 value: 0.9209583313765042 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2331 - Accuracy: 0.921 - F1: 0.9210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8643 | 1.0 | 250 | 0.3494 | 0.8965 | 0.8909 | | 0.2629 | 2.0 | 500 | 0.2331 | 0.921 | 0.9210 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,846
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DuyTuan/distilbert-base-uncased-finetuned-emotion
2023-10-04T06:04:15.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
DuyTuan
null
null
DuyTuan/distilbert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-18T08:35:29
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: split metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9232244925505232 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2199 - Accuracy: 0.923 - F1: 0.9232 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8288 | 1.0 | 250 | 0.3054 | 0.9065 | 0.9036 | | 0.2521 | 2.0 | 500 | 0.2199 | 0.923 | 0.9232 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
1,802
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MrPark97/distillbert-base-uncased-finetuned-clinc
2023-05-18T14:37:05.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
MrPark97
null
null
MrPark97/distillbert-base-uncased-finetuned-clinc
0
2
transformers
2023-05-18T09:15:51
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distillbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distillbert-base-uncased-finetuned-clinc 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: - Loss: 0.7720 - Accuracy: 0.9181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2887 | 0.7419 | | 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 | | 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 | | 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 | | 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3
1,616
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AlexC98/commitRoBertaGood
2023-05-18T13:08:21.000Z
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AlexC98
null
null
AlexC98/commitRoBertaGood
0
2
transformers
2023-05-18T11:08:56
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: commitRoBertaGood results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # commitRoBertaGood 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: 0.9193 - Accuracy: 0.8242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 371 | 0.5855 | 0.7091 | | 0.5618 | 2.0 | 742 | 0.7041 | 0.7939 | | 0.4278 | 3.0 | 1113 | 0.7003 | 0.8182 | | 0.4278 | 4.0 | 1484 | 0.9193 | 0.8242 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,513
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HasinMDG/all-distilroberta-v1-IPTC-L1
2023-05-18T14:54:50.000Z
[ "sentence-transformers", "pytorch", "roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
HasinMDG
null
null
HasinMDG/all-distilroberta-v1-IPTC-L1
0
2
sentence-transformers
2023-05-18T12:52:01
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # HasinMDG/all-distilroberta-v1-IPTC-L1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("HasinMDG/all-distilroberta-v1-IPTC-L1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
1,563
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LamaAldakhil/SL-CvT
2023-05-18T20:27:17.000Z
[ "transformers", "pytorch", "tensorboard", "cvt", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
LamaAldakhil
null
null
LamaAldakhil/SL-CvT
0
2
transformers
2023-05-18T12:55:22
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - f1 - accuracy model-index: - name: SL-CvT results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: F1 type: f1 value: 0.9297928229609359 - name: Accuracy type: accuracy value: 0.9316640584246219 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SL-CvT This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3430 - F1: 0.9298 - Roc Auc: 0.9777 - Accuracy: 0.9317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 1.2379 | 1.0 | 60 | 1.0716 | 0.6422 | 0.7323 | 0.7246 | | 1.0186 | 2.0 | 120 | 0.8477 | 0.6425 | 0.7879 | 0.7293 | | 0.9433 | 3.0 | 180 | 0.7473 | 0.7060 | 0.8454 | 0.7538 | | 0.8644 | 4.0 | 240 | 0.6831 | 0.7188 | 0.8696 | 0.7663 | | 0.7985 | 5.0 | 300 | 0.6420 | 0.7409 | 0.8943 | 0.7799 | | 0.7322 | 6.0 | 360 | 0.5713 | 0.7886 | 0.9196 | 0.8101 | | 0.725 | 7.0 | 420 | 0.5311 | 0.7989 | 0.9324 | 0.8190 | | 0.6529 | 8.0 | 480 | 0.5246 | 0.7852 | 0.9404 | 0.8117 | | 0.6224 | 9.0 | 540 | 0.4598 | 0.8282 | 0.9517 | 0.8440 | | 0.6315 | 10.0 | 600 | 0.4363 | 0.8457 | 0.9585 | 0.8529 | | 0.5651 | 11.0 | 660 | 0.4437 | 0.8323 | 0.9564 | 0.8503 | | 0.574 | 12.0 | 720 | 0.4003 | 0.8531 | 0.9617 | 0.8638 | | 0.5269 | 13.0 | 780 | 0.3901 | 0.8676 | 0.9671 | 0.8722 | | 0.5138 | 14.0 | 840 | 0.3984 | 0.8607 | 0.9685 | 0.8732 | | 0.4839 | 15.0 | 900 | 0.3763 | 0.8683 | 0.9701 | 0.8769 | | 0.463 | 16.0 | 960 | 0.3398 | 0.8837 | 0.9718 | 0.8894 | | 0.4767 | 17.0 | 1020 | 0.3293 | 0.8846 | 0.9738 | 0.8915 | | 0.4985 | 18.0 | 1080 | 0.3350 | 0.8852 | 0.9763 | 0.8863 | | 0.4657 | 19.0 | 1140 | 0.3369 | 0.8872 | 0.9746 | 0.8951 | | 0.4514 | 20.0 | 1200 | 0.3213 | 0.8880 | 0.9750 | 0.8925 | | 0.4207 | 21.0 | 1260 | 0.3175 | 0.8943 | 0.9771 | 0.8978 | | 0.4522 | 22.0 | 1320 | 0.3229 | 0.8970 | 0.9767 | 0.8983 | | 0.4328 | 23.0 | 1380 | 0.3121 | 0.8948 | 0.9791 | 0.8978 | | 0.3942 | 24.0 | 1440 | 0.3111 | 0.8993 | 0.9765 | 0.9030 | | 0.4414 | 25.0 | 1500 | 0.3062 | 0.9032 | 0.9763 | 0.9061 | | 0.3608 | 26.0 | 1560 | 0.3099 | 0.8997 | 0.9787 | 0.9014 | | 0.3729 | 27.0 | 1620 | 0.3050 | 0.9029 | 0.9783 | 0.9082 | | 0.393 | 28.0 | 1680 | 0.2970 | 0.9090 | 0.9797 | 0.9108 | | 0.402 | 29.0 | 1740 | 0.2986 | 0.9087 | 0.9793 | 0.9113 | | 0.3697 | 30.0 | 1800 | 0.3384 | 0.8968 | 0.9769 | 0.9025 | | 0.3502 | 31.0 | 1860 | 0.3035 | 0.9058 | 0.9789 | 0.9103 | | 0.3653 | 32.0 | 1920 | 0.3127 | 0.9024 | 0.9788 | 0.9025 | | 0.3898 | 33.0 | 1980 | 0.3222 | 0.9050 | 0.9778 | 0.9061 | | 0.317 | 34.0 | 2040 | 0.3013 | 0.9124 | 0.9798 | 0.9139 | | 0.3166 | 35.0 | 2100 | 0.3185 | 0.9095 | 0.9775 | 0.9134 | | 0.3771 | 36.0 | 2160 | 0.3067 | 0.9049 | 0.9782 | 0.9066 | | 0.3487 | 37.0 | 2220 | 0.2948 | 0.9118 | 0.9801 | 0.9134 | | 0.3202 | 38.0 | 2280 | 0.2916 | 0.9168 | 0.9788 | 0.9186 | | 0.3163 | 39.0 | 2340 | 0.3149 | 0.9141 | 0.9777 | 0.9155 | | 0.3605 | 40.0 | 2400 | 0.2964 | 0.9192 | 0.9797 | 0.9207 | | 0.3636 | 41.0 | 2460 | 0.3142 | 0.9111 | 0.9810 | 0.9134 | | 0.3454 | 42.0 | 2520 | 0.3133 | 0.9111 | 0.9792 | 0.9113 | | 0.3561 | 43.0 | 2580 | 0.3090 | 0.9073 | 0.9804 | 0.9077 | | 0.3136 | 44.0 | 2640 | 0.3236 | 0.9144 | 0.9782 | 0.9176 | | 0.3529 | 45.0 | 2700 | 0.3054 | 0.9175 | 0.9800 | 0.9202 | | 0.2987 | 46.0 | 2760 | 0.2944 | 0.9222 | 0.9802 | 0.9233 | | 0.2966 | 47.0 | 2820 | 0.3215 | 0.9201 | 0.9786 | 0.9233 | | 0.3203 | 48.0 | 2880 | 0.3150 | 0.9219 | 0.9797 | 0.9244 | | 0.2821 | 49.0 | 2940 | 0.3072 | 0.9273 | 0.9800 | 0.9291 | | 0.2852 | 50.0 | 3000 | 0.3265 | 0.9155 | 0.9792 | 0.9176 | | 0.3544 | 51.0 | 3060 | 0.3175 | 0.9150 | 0.9802 | 0.9150 | | 0.3327 | 52.0 | 3120 | 0.3134 | 0.9222 | 0.9802 | 0.9244 | | 0.2877 | 53.0 | 3180 | 0.3222 | 0.9154 | 0.9805 | 0.9165 | | 0.3089 | 54.0 | 3240 | 0.3045 | 0.9248 | 0.9811 | 0.9259 | | 0.2904 | 55.0 | 3300 | 0.3301 | 0.9175 | 0.9787 | 0.9186 | | 0.2821 | 56.0 | 3360 | 0.3069 | 0.9206 | 0.9810 | 0.9218 | | 0.321 | 57.0 | 3420 | 0.3209 | 0.9254 | 0.9800 | 0.9270 | | 0.2995 | 58.0 | 3480 | 0.3281 | 0.9202 | 0.9802 | 0.9233 | | 0.2683 | 59.0 | 3540 | 0.3263 | 0.9174 | 0.9802 | 0.9202 | | 0.3021 | 60.0 | 3600 | 0.3484 | 0.9170 | 0.9788 | 0.9186 | | 0.3262 | 61.0 | 3660 | 0.3270 | 0.9151 | 0.9807 | 0.9165 | | 0.2329 | 62.0 | 3720 | 0.3280 | 0.9211 | 0.9807 | 0.9233 | | 0.2935 | 63.0 | 3780 | 0.3296 | 0.9244 | 0.9807 | 0.9264 | | 0.2856 | 64.0 | 3840 | 0.3323 | 0.9209 | 0.9811 | 0.9218 | | 0.2829 | 65.0 | 3900 | 0.3390 | 0.9200 | 0.9802 | 0.9218 | | 0.3044 | 66.0 | 3960 | 0.3324 | 0.9215 | 0.9799 | 0.9228 | | 0.2767 | 67.0 | 4020 | 0.3496 | 0.9150 | 0.9778 | 0.9160 | | 0.2936 | 68.0 | 4080 | 0.3378 | 0.9257 | 0.9790 | 0.9275 | | 0.2884 | 69.0 | 4140 | 0.3493 | 0.9227 | 0.9790 | 0.9249 | | 0.2906 | 70.0 | 4200 | 0.3408 | 0.9259 | 0.9794 | 0.9275 | | 0.2542 | 71.0 | 4260 | 0.3559 | 0.9233 | 0.9769 | 0.9249 | | 0.2557 | 72.0 | 4320 | 0.3481 | 0.9237 | 0.9779 | 0.9254 | | 0.2266 | 73.0 | 4380 | 0.3518 | 0.9208 | 0.9781 | 0.9223 | | 0.2771 | 74.0 | 4440 | 0.3544 | 0.9231 | 0.9776 | 0.9254 | | 0.2747 | 75.0 | 4500 | 0.3469 | 0.9270 | 0.9780 | 0.9285 | | 0.2443 | 76.0 | 4560 | 0.3513 | 0.9216 | 0.9767 | 0.9233 | | 0.2859 | 77.0 | 4620 | 0.3456 | 0.9234 | 0.9771 | 0.9254 | | 0.2677 | 78.0 | 4680 | 0.3474 | 0.9239 | 0.9780 | 0.9254 | | 0.2492 | 79.0 | 4740 | 0.3513 | 0.9235 | 0.9778 | 0.9254 | | 0.2532 | 80.0 | 4800 | 0.3524 | 0.9210 | 0.9773 | 0.9233 | | 0.2646 | 81.0 | 4860 | 0.3529 | 0.9240 | 0.9784 | 0.9238 | | 0.2842 | 82.0 | 4920 | 0.3433 | 0.9260 | 0.9777 | 0.9280 | | 0.2872 | 83.0 | 4980 | 0.3584 | 0.9272 | 0.9771 | 0.9285 | | 0.2678 | 84.0 | 5040 | 0.3430 | 0.9298 | 0.9777 | 0.9317 | | 0.2705 | 85.0 | 5100 | 0.3534 | 0.9268 | 0.9777 | 0.9291 | | 0.2605 | 86.0 | 5160 | 0.3574 | 0.9272 | 0.9777 | 0.9296 | | 0.2572 | 87.0 | 5220 | 0.3426 | 0.9273 | 0.9781 | 0.9291 | | 0.2646 | 88.0 | 5280 | 0.3472 | 0.9234 | 0.9789 | 0.9244 | | 0.2831 | 89.0 | 5340 | 0.3433 | 0.9272 | 0.9779 | 0.9291 | | 0.277 | 90.0 | 5400 | 0.3441 | 0.9263 | 0.9789 | 0.9280 | | 0.2584 | 91.0 | 5460 | 0.3432 | 0.9236 | 0.9788 | 0.9249 | | 0.2703 | 92.0 | 5520 | 0.3409 | 0.9248 | 0.9789 | 0.9259 | | 0.2811 | 93.0 | 5580 | 0.3449 | 0.9215 | 0.9795 | 0.9228 | | 0.2786 | 94.0 | 5640 | 0.3465 | 0.9260 | 0.9789 | 0.9280 | | 0.267 | 95.0 | 5700 | 0.3472 | 0.9260 | 0.9791 | 0.9275 | | 0.2695 | 96.0 | 5760 | 0.3500 | 0.9268 | 0.9786 | 0.9285 | | 0.279 | 97.0 | 5820 | 0.3582 | 0.9249 | 0.9782 | 0.9270 | | 0.2774 | 98.0 | 5880 | 0.3486 | 0.9251 | 0.9790 | 0.9270 | | 0.2512 | 99.0 | 5940 | 0.3514 | 0.9287 | 0.9786 | 0.9306 | | 0.2218 | 100.0 | 6000 | 0.3482 | 0.9269 | 0.9789 | 0.9285 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
9,887
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phoen1x/T5-Finetuned-INlegaldocsum
2023-05-18T14:31:52.000Z
[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
phoen1x
null
null
phoen1x/T5-Finetuned-INlegaldocsum
0
2
transformers
2023-05-18T14:30:58
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: T5-Finetuned-INlegaldocsum results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # T5-Finetuned-INlegaldocsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.6619 - Validation Loss: 2.2688 - Train Rougel: tf.Tensor(0.1290423, shape=(), dtype=float32) - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:---------------------------------------------:|:-----:| | 2.6619 | 2.2688 | tf.Tensor(0.1290423, shape=(), dtype=float32) | 0 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.12.1
1,652
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AnanthZeke/tabert-4k-naamapadam
2023-05-18T16:40:32.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
AnanthZeke
null
null
AnanthZeke/tabert-4k-naamapadam
0
2
transformers
2023-05-18T15:13:11
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: tabert-4k-naamapadam results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabert-4k-naamapadam This model is a fine-tuned version of [livinNector/tabert-4k](https://huggingface.co/livinNector/tabert-4k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2805 - Precision: 0.7758 - Recall: 0.8034 - F1: 0.7894 - Accuracy: 0.9077 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4467 | 0.05 | 400 | 0.3882 | 0.7144 | 0.6655 | 0.6891 | 0.8755 | | 0.3775 | 0.1 | 800 | 0.3540 | 0.7122 | 0.7155 | 0.7138 | 0.8845 | | 0.3571 | 0.15 | 1200 | 0.3432 | 0.7329 | 0.7266 | 0.7297 | 0.8872 | | 0.3461 | 0.21 | 1600 | 0.3360 | 0.7252 | 0.7368 | 0.7309 | 0.8893 | | 0.3456 | 0.26 | 2000 | 0.3359 | 0.7388 | 0.7470 | 0.7428 | 0.8896 | | 0.3318 | 0.31 | 2400 | 0.3298 | 0.7460 | 0.7435 | 0.7447 | 0.8908 | | 0.326 | 0.36 | 2800 | 0.3255 | 0.7490 | 0.7391 | 0.7440 | 0.8940 | | 0.3264 | 0.41 | 3200 | 0.3243 | 0.7493 | 0.7605 | 0.7549 | 0.8953 | | 0.3189 | 0.46 | 3600 | 0.3231 | 0.7305 | 0.7715 | 0.7504 | 0.8936 | | 0.3119 | 0.51 | 4000 | 0.3125 | 0.7645 | 0.7525 | 0.7584 | 0.8985 | | 0.3111 | 0.57 | 4400 | 0.3100 | 0.7479 | 0.7729 | 0.7602 | 0.8970 | | 0.3088 | 0.62 | 4800 | 0.3148 | 0.7510 | 0.7749 | 0.7628 | 0.8966 | | 0.3047 | 0.67 | 5200 | 0.3089 | 0.7581 | 0.7728 | 0.7654 | 0.8981 | | 0.3054 | 0.72 | 5600 | 0.3073 | 0.7615 | 0.7709 | 0.7662 | 0.8990 | | 0.3028 | 0.77 | 6000 | 0.3066 | 0.7466 | 0.7835 | 0.7646 | 0.8984 | | 0.3007 | 0.82 | 6400 | 0.3035 | 0.7555 | 0.7791 | 0.7671 | 0.8995 | | 0.2923 | 0.87 | 6800 | 0.3004 | 0.7647 | 0.7829 | 0.7737 | 0.9008 | | 0.2927 | 0.93 | 7200 | 0.3050 | 0.7700 | 0.7646 | 0.7673 | 0.9002 | | 0.2949 | 0.98 | 7600 | 0.2979 | 0.7686 | 0.7723 | 0.7704 | 0.9014 | | 0.2758 | 1.03 | 8000 | 0.3013 | 0.7713 | 0.7783 | 0.7748 | 0.9030 | | 0.2699 | 1.08 | 8400 | 0.3019 | 0.7503 | 0.7997 | 0.7742 | 0.9017 | | 0.2688 | 1.13 | 8800 | 0.3002 | 0.7593 | 0.7940 | 0.7762 | 0.9017 | | 0.2625 | 1.18 | 9200 | 0.2926 | 0.7590 | 0.7941 | 0.7762 | 0.9033 | | 0.2671 | 1.23 | 9600 | 0.2922 | 0.7640 | 0.8019 | 0.7825 | 0.9043 | | 0.267 | 1.29 | 10000 | 0.2895 | 0.7719 | 0.7877 | 0.7797 | 0.9044 | | 0.2611 | 1.34 | 10400 | 0.2897 | 0.7704 | 0.7978 | 0.7839 | 0.9053 | | 0.2666 | 1.39 | 10800 | 0.2896 | 0.7688 | 0.7887 | 0.7786 | 0.9042 | | 0.2563 | 1.44 | 11200 | 0.2894 | 0.7672 | 0.7981 | 0.7823 | 0.9045 | | 0.2598 | 1.49 | 11600 | 0.2841 | 0.7705 | 0.7960 | 0.7831 | 0.9058 | | 0.2549 | 1.54 | 12000 | 0.2854 | 0.7695 | 0.7975 | 0.7832 | 0.9065 | | 0.2558 | 1.59 | 12400 | 0.2873 | 0.7619 | 0.8108 | 0.7856 | 0.9045 | | 0.2564 | 1.65 | 12800 | 0.2863 | 0.7757 | 0.7897 | 0.7826 | 0.9062 | | 0.2618 | 1.7 | 13200 | 0.2860 | 0.7778 | 0.7899 | 0.7838 | 0.9066 | | 0.2659 | 1.75 | 13600 | 0.2831 | 0.7748 | 0.8013 | 0.7879 | 0.9073 | | 0.254 | 1.8 | 14000 | 0.2811 | 0.7761 | 0.7978 | 0.7868 | 0.9079 | | 0.2628 | 1.85 | 14400 | 0.2807 | 0.7713 | 0.8028 | 0.7868 | 0.9069 | | 0.2552 | 1.9 | 14800 | 0.2806 | 0.7756 | 0.7990 | 0.7872 | 0.9077 | | 0.2568 | 1.95 | 15200 | 0.2805 | 0.7758 | 0.8034 | 0.7894 | 0.9077 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3
4,925
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AustinCarthy/Onlyphish_10K_fromP_BFall_10KGen_topP_0.75
2023-05-18T17:16:06.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Onlyphish_10K_fromP_BFall_10KGen_topP_0.75
0
2
transformers
2023-05-18T15:33:34
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Onlyphish_10K_fromP_BFall_10KGen_topP_0.75 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Onlyphish_10K_fromP_BFall_10KGen_topP_0.75 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0759 - Accuracy: 0.9929 - F1: 0.9193 - Precision: 1.0 - Recall: 0.8506 - Roc Auc Score: 0.9253 - Tpr At Fpr 0.01: 0.8776 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0055 | 1.0 | 13125 | 0.0436 | 0.9901 | 0.8844 | 0.9933 | 0.797 | 0.8984 | 0.7488 | | 0.0032 | 2.0 | 26250 | 0.1145 | 0.9853 | 0.8171 | 0.9994 | 0.691 | 0.8455 | 0.756 | | 0.0025 | 3.0 | 39375 | 0.0705 | 0.9919 | 0.9076 | 0.9978 | 0.8324 | 0.9162 | 0.8332 | | 0.0018 | 4.0 | 52500 | 0.0848 | 0.9919 | 0.9065 | 0.9998 | 0.8292 | 0.9146 | 0.8506 | | 0.0008 | 5.0 | 65625 | 0.0759 | 0.9929 | 0.9193 | 1.0 | 0.8506 | 0.9253 | 0.8776 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,243
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mingmingmom888/distilbert_classifier_newsgroups
2023-05-18T15:56:51.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
mingmingmom888
null
null
mingmingmom888/distilbert_classifier_newsgroups
0
2
transformers
2023-05-18T15:56:19
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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AlexC98/testing
2023-05-18T17:28:37.000Z
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
text-classification
AlexC98
null
null
AlexC98/testing
0
2
transformers
2023-05-18T17:12:03
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: testing results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # testing This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5427 - Accuracy: 0.7455 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 47 | 0.6397 | 0.6364 | | No log | 2.0 | 94 | 0.6157 | 0.6788 | | No log | 3.0 | 141 | 0.5956 | 0.6788 | | No log | 4.0 | 188 | 0.5866 | 0.6848 | | No log | 5.0 | 235 | 0.5727 | 0.6788 | | No log | 6.0 | 282 | 0.5663 | 0.6970 | | No log | 7.0 | 329 | 0.5610 | 0.7091 | | No log | 8.0 | 376 | 0.5548 | 0.7091 | | No log | 9.0 | 423 | 0.5536 | 0.7212 | | No log | 10.0 | 470 | 0.5486 | 0.7273 | | 0.583 | 11.0 | 517 | 0.5451 | 0.7273 | | 0.583 | 12.0 | 564 | 0.5468 | 0.7333 | | 0.583 | 13.0 | 611 | 0.5423 | 0.7394 | | 0.583 | 14.0 | 658 | 0.5396 | 0.7394 | | 0.583 | 15.0 | 705 | 0.5466 | 0.7394 | | 0.583 | 16.0 | 752 | 0.5411 | 0.7455 | | 0.583 | 17.0 | 799 | 0.5427 | 0.7455 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
2,298
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oyesaurav/dwellbert
2023-05-22T07:30:04.000Z
[ "transformers", "tf", "distilbert", "text-classification", "ditilbert", "text classification", "clinical notes", "wellnation", "en", "endpoints_compatible", "region:us" ]
text-classification
oyesaurav
null
null
oyesaurav/dwellbert
1
2
transformers
2023-05-18T17:39:42
--- language: - en tags: - ditilbert - text classification - clinical notes - wellnation --- <pre> labels map = { "0": "Gastroenterology", "1": "Neurology", "2": "Orthopedic", "3": "Radiology", "4": "Urology" } </pre> <h2><i>The fine tuned model has been trained on around 2300 medical transcriptions, to classify medical specialty. More classes will be added as data would be available.</i></h2>
413
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carinavdzee/distilbert_classifier_newsgroups
2023-05-18T18:05:06.000Z
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
carinavdzee
null
null
carinavdzee/distilbert_classifier_newsgroups
0
2
transformers
2023-05-18T18:04:34
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_classifier_newsgroups 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1908, '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 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
1,471
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trinadutta/distilbert-base-uncased-finetuned-stsb
2023-05-18T21:44:50.000Z
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
trinadutta
null
null
trinadutta/distilbert-base-uncased-finetuned-stsb
0
2
transformers
2023-05-18T18:27:24
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: stsb split: validation args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8703919468681796 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5388 - Pearson: 0.8740 - Spearmanr: 0.8704 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.6683 | 0.8599 | 0.8575 | | 1.0348 | 2.0 | 720 | 0.5413 | 0.8715 | 0.8685 | | 0.3974 | 3.0 | 1080 | 0.5560 | 0.8725 | 0.8692 | | 0.3974 | 4.0 | 1440 | 0.5666 | 0.8737 | 0.8703 | | 0.2516 | 5.0 | 1800 | 0.5388 | 0.8740 | 0.8704 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
2,009
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qcz/en-fr-UFAL-medical
2023-05-18T19:15:45.000Z
[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "en", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
qcz
null
null
qcz/en-fr-UFAL-medical
0
2
transformers
2023-05-18T19:09:39
--- language: - en - fr license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: en-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # en-fr This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5956 - Bleu: 53.2928 - Gen Len: 53.437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.11.0 - Tokenizers 0.13.3
1,152
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lcrodriguez/test_trainer
2023-05-18T20:54:55.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
lcrodriguez
null
null
lcrodriguez/test_trainer
0
2
transformers
2023-05-18T20:32:41
--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: test_trainer results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: test args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.539 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 2.6184 - Accuracy: 0.539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 2.0325 | 0.499 | | No log | 2.0 | 250 | 2.3086 | 0.547 | | No log | 3.0 | 375 | 2.6184 | 0.539 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,770
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lcrodriguez/learn-finetuning-bert
2023-05-18T21:00:57.000Z
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
lcrodriguez
null
null
lcrodriguez/learn-finetuning-bert
0
2
transformers
2023-05-18T20:58:35
--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full model-index: - name: learn-finetuning-bert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # learn-finetuning-bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,050
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Forna/bert-base-uncased-finetuned-emotion
2023-05-18T21:22:49.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
Forna
null
null
Forna/bert-base-uncased-finetuned-emotion
0
2
transformers
2023-05-18T21:02:04
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: bert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2082 - Accuratezza: 0.918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuratezza | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | No log | 1.0 | 250 | 0.2918 | 0.9045 | | 0.5145 | 2.0 | 500 | 0.2082 | 0.918 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,441
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AustinCarthy/Baseline_10Kphish_benignWinter_20_20_20
2023-05-18T22:14:26.000Z
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
AustinCarthy
null
null
AustinCarthy/Baseline_10Kphish_benignWinter_20_20_20
0
2
transformers
2023-05-18T21:07:42
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: Baseline_10Kphish_benignWinter_20_20_20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Baseline_10Kphish_benignWinter_20_20_20 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0869 - Accuracy: 0.991 - F1: 0.8960 - Precision: 0.9966 - Recall: 0.8138 - Roc Auc Score: 0.9068 - Tpr At Fpr 0.01: 0.7918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0115 | 1.0 | 6563 | 0.0605 | 0.9872 | 0.8462 | 0.9938 | 0.7368 | 0.8683 | 0.6832 | | 0.006 | 2.0 | 13126 | 0.0538 | 0.9911 | 0.8975 | 0.9946 | 0.8176 | 0.9087 | 0.7928 | | 0.0033 | 3.0 | 19689 | 0.0496 | 0.9917 | 0.9049 | 0.9959 | 0.8292 | 0.9145 | 0.805 | | 0.001 | 4.0 | 26252 | 0.0791 | 0.9911 | 0.8970 | 0.9959 | 0.816 | 0.9079 | 0.7806 | | 0.0002 | 5.0 | 32815 | 0.0869 | 0.991 | 0.8960 | 0.9966 | 0.8138 | 0.9068 | 0.7918 | ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
2,239
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petersa2/distilbert-code
2023-05-19T03:46:45.000Z
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
petersa2
null
null
petersa2/distilbert-code
0
2
transformers
2023-05-18T21:17:49
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: distilbert-code results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.56 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-code This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6881 - Accuracy: 0.56 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.6890 | 0.52 | | No log | 2.0 | 14 | 0.6881 | 0.56 | ### Framework versions - Transformers 4.11.3 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.10.3
1,623
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wiorz/bert_legal_binary_sm_pair
2023-05-18T22:54:16.000Z
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
wiorz
null
null
wiorz/bert_legal_binary_sm_pair
0
2
transformers
2023-05-18T22:53:40
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert_legal_binary_sm_pair results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_legal_binary_sm_pair This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 20 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
1,116
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