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", device_map="auto") - 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 | |
| config: sst2 | |
| split: validation | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8967889908256881 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmIzNTQzNTdmOWYyNmI3MDZiNTljNjU3ZmM2OTFmMzQyZTE1NDY4MjJkYWQ2ZmZiYTJhYTU5NGFkZDYwOTQ0OCIsInZlcnNpb24iOjF9.KH3XYA5ERa68EQUBPm1Jbw5S10dpjbeTR2Dc5d8NoVPue4h3tdlbmN3FfyOU1dQ4tHnIDwHqxiJJnNGdM9cEDg | |
| - name: Precision | |
| type: precision | |
| value: 0.8898678414096917 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODgwZGQ2ODBhZDI2ZjM3M2Q4OTQyNjViOTY3MDk3YjAwZjg0ODhlM2M4N2NmYmQzOTlkOGVkMDgwY2RmOTE1MCIsInZlcnNpb24iOjF9.potMTyGROXNJq0zC_kC9lAR3oqI1nOnWZ09XNLEyPbgzmOQ_jvJWH7U7gzkd6BlhkFrnttkPl1O4VOvAAuQKCg | |
| - name: Recall | |
| type: recall | |
| value: 0.9099099099099099 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzZjY2I2NjBkMjVjMTk2YzJmMDIzYjY3NzRmMGRmYTM2Y2U0Mjc0NDZjMjE3MWZlYzJjMGZjYmExYjk0ZjEyNyIsInZlcnNpb24iOjF9.69DayhBa8EEbBPCWwonMYawLBNeH5f6cv6mPeZ0jKq0yFpMGXcZJETdh_TMifnkMKQQZs8C_CpSBz8DJnh_IAA | |
| - name: AUC | |
| type: auc | |
| value: 0.9672186789593331 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGY0ZjJiZTNmODdiMzZkZGFiOWQ3ZDg4MjVmOGVjZDNkMzE5ZGVkMWUzYzY5ZDhmOTg0MzNjOGQ4N2IzZDM2OCIsInZlcnNpb24iOjF9.tpUZmfQZ0TREqxkSDfA0Tiw1E2cn1FoU1yFUAbY6BHko-ay3-cTSHkBUObGnVpeeCaHMSuTap30lB4qD3qRMAg | |
| - name: F1 | |
| type: f1 | |
| value: 0.8997772828507795 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjU4MTBkMjllYzk2ZmE2Y2MxODhlODljNTEyZjlhYTQxMDAwZGYzZTg1Mzc4NjBlZTk0ODVhMWJhN2FlZmMzZCIsInZlcnNpb24iOjF9.VcX2afBIJBFSJbZPZvuxx1GCNuIjB3zyQ0G-UGhvjoOLE23s23dWBiTT00VZHCOPqmNpIrKJ_ZaDqMmSPyYQBA | |
| - name: loss | |
| type: loss | |
| value: 0.30092036724090576 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDlhMDUyOGE2NTA4ZGY0MDY1Y2E2MzFkYzU4NjRjZmJmZjFlYzkwY2ZlNWE2OTkxYzQyMzcxMzA5ZDRiYWMxYyIsInZlcnNpb24iOjF9.oWS9P7t9o6FA0vqitSVLOmhmOfnAcFqOff_I_zoIFfF3V8OURstz6tP_-MxYnLeNUCuYSGFDiXaZUaYuvLc3Bw | |
| <!-- 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 | |