Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/binary-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/binary-classification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: autoevaluate-binary-classification | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: glue | |
| type: glue | |
| args: sst2 | |
| metrics: | |
| - type: accuracy | |
| value: 0.8967889908256881 | |
| name: Accuracy | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: glue | |
| type: glue | |
| config: sst2 | |
| split: validation | |
| metrics: | |
| - type: accuracy | |
| value: 0.8967889908256881 | |
| name: Accuracy | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTZmNGE1N2FjODM3OGJiM2Q2NTY5MzZjNGFhNGVjYzcwOTlkMzVhYjdmOTgwY2Y1NzMyZjY0NzAxMzZkMjM4NyIsInZlcnNpb24iOjF9.LabPe-QWLUUJdPyQ0Ki9rHq74opfAO1fxvu2FjUFiY9zhxAe0RKNjZRHPbrF10249Z3kDZSAq2yzQ1TjKvoLBQ | |
| - type: precision | |
| value: 0.8898678414096917 | |
| name: Precision | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTczZjUwY2MzNTMzY2VlMjFmZGI2MzAwNTEwM2IzYWVkYmFiNjk0MDM3YmYzYjFmNGM3NWI5NDIzODJjMTA1ZCIsInZlcnNpb24iOjF9.3RC343Rtep7yxGH82c1WV2IAVqhJTRoOwiwFVp_w0K0JK_dTqnfEylLb1yMt367ztvkhhOgRn4i9GsL4ZNC5BQ | |
| - type: recall | |
| value: 0.9099099099099099 | |
| name: Recall | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmM4M2Y3YTVjOTlhZjc2OGUxMzFhNGI3YzM4MDI0NDMwMmQyMmRmY2MyMTI5ZTdmYWVjMTlmYWE0N2Q0ZjJiNyIsInZlcnNpb24iOjF9.lMKosw258_E40HdqY8BFyWVJYAMx4cpVyYusGEqN429_cv3DzeIMaOr00trGsJX3BIqr-j5ScjLVV79f5nK2CA | |
| - type: auc | |
| value: 0.9672186789593331 | |
| name: AUC | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzY1YmM4YjJhNTY2ZmIyYmI5ZTBjZjc3MDZiMzQ3ZTEyZWQ1M2I4ZTk4OGYwNzZiY2VlODRkODRjNTg2MDNmMSIsInZlcnNpb24iOjF9.tO3GQ5Rgl26zHz18-yR2wtcajmb_MEPNCZiA1Exz4255-m1iDFyMPM2Pw4s75xUSXWzsF--bo6eqmCLo4yjkBw | |
| - type: f1 | |
| value: 0.8997772828507795 | |
| name: F1 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmM0ZjhjZWY2ZGZiYWZhOTY2OWUwNzcxMTRlNjU4MDMyMWViMjg2YzE0YzBiMzVlYTU2ODkyZWY0MzcxOWJlOCIsInZlcnNpb24iOjF9.sySuyn4j72Gt3wstru118StL7pzGgZKzAPtE0FM7HVfdBrVXwZckKaUmoQR-nKaVynbo1h4mykNdM-_MwmLlCA | |
| - type: loss | |
| value: 0.30092036724090576 | |
| name: loss | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDQ2ZjJiMjVhNTMxZGIxMTFlMjVhYTQyOGI2YjgyOTI3OTQ4NGU0ZWYxMDY2MmI1OGNiNDcwNTU3MmEzM2YzZSIsInZlcnNpb24iOjF9.MGCrOvwyOdMQ91z2pzgsIxS-PMCZy2YwNX7IuMNAVokRhTSGUYtFt-8px1Dv9w39IT6ZbySZ7kQQKz6kK8HWAQ | |
| - type: matthews_correlation | |
| value: 0.793630584795814 | |
| name: matthews_correlation | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGY5ODMyZjc4MTk0NWU1YTRmNGI5NDU0ZGRlMDEwY2ZhN2YzMjAxNDE2MTY4ZTI2OWZjMzkwMzc5NTY3NTlkMSIsInZlcnNpb24iOjF9.1WB_1AIkuk68pphfqpqB_T1VpM3wJPe7mNGOvaDANcek7TKUFuT6kA8J1h1SICS_80mdXDI4yJGGZy3CZwpXDQ | |
| <!-- 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. --> | |
| # binary-classification | |
| 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: 0.3009 | |
| - Accuracy: 0.8968 | |
| ## 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: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 | | |
| ### Framework versions | |
| - Transformers 4.19.2 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.2.2 | |
| - Tokenizers 0.12.1 | |