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.eyJoYXNoIjoiOTFlOTdiMDAzZGViNDg5M2I3NTgwMjc1ZjkzY2MwOTZjOWZiYmU0NmRmM2IwNDY5YmRmNTVlNDY0NDE2NWZmZCIsInZlcnNpb24iOjF9.6d1x30eSEs8YfDPPtICHQfXEg4VnrGUdVSWLf2_vUldUToSAw74qAUWSbk6sEk1mzPDPVw4RczluYWU0IZUdBw | |
| - type: precision | |
| value: 0.8898678414096917 | |
| name: Precision | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzhmZmViMzkzODA3ODExNWY1MDg0NzBlNGZhYTZlYzFhZDc1ZGI0Nzk1NWI0ZjBlODlhODQ4OGVmN2MwYWNhNiIsInZlcnNpb24iOjF9.1fR41yn4FE80VVRfG9dWm5icW16gBg4o_lSmnHqe3nIhenIIkoNCfks6VqNfd0QWSMM4TpUPRku5q1iiz2aIDQ | |
| - type: recall | |
| value: 0.9099099099099099 | |
| name: Recall | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzk0MmVjYWU4NzkzZWIyMGJhNzRlNWFkNGJmZDcyYzMxZmNiMGYzYzYyYzA5NjhhN2I4OWEwNDAzZmYwMTJkYSIsInZlcnNpb24iOjF9.zQD5ytQnIJ4UEx5lFHWny8NuPPp79Z6O7sJJsWfqR7L6cwQUx9pycU97zyL9Rw8yRcngZbgyDKldmhn5vR8dCw | |
| - type: auc | |
| value: 0.9672186789593331 | |
| name: AUC | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGZlMGMwODQ0NmMwNDE3NzU1YWRkYTMyMGI0NWI4MDA2NTFkNWI1MWM4NjNmYTdkMjA5OTRmY2U3NDUwMWFjNyIsInZlcnNpb24iOjF9.PUmHEc87_81R2PoIxmcVLulheA475I1IIVqIP-9huiFL5mhzP6S4Li9fBe-SpcGLmWHvL1G-HyB6dMsE8e9HDw | |
| - type: f1 | |
| value: 0.8997772828507795 | |
| name: F1 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzkwZjYzYjI4ZGJjZjk0NWFiY2IxOTEyYmJmOGRjM2IwNzNmZTdlMjFjNzc1MmNmZTA5MDcwMTgzZTAzOWVmNyIsInZlcnNpb24iOjF9.GGM0p5vy4tdmWKk6aFLfnQHAS2xYl7VBHBsuckgbhP_UYcZp0bGorbHQkHAdB2pJaOjtc1sSUR5AV30eCBayDw | |
| - type: loss | |
| value: 0.30092036724090576 | |
| name: loss | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmQ5N2Y4NDYzNzUyMGJlODQxZDA0Y2QyNmE0M2IyM2VhOTVlYWVjMDZmZmE3NWMwMTg0MGE4YTQwNzVmMjA2YyIsInZlcnNpb24iOjF9.E73R7ch2UnYuAA4KQzoefY0u6sgJ0FP-h2IWss2L0tPHXxAycSGmADAjUmBjfPWX3-XqhcZwm1-5QB-q6ujHDA | |
| - type: matthews_correlation | |
| value: 0.793630584795814 | |
| name: matthews_correlation | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Q5YzEyN2NhYzg2ZDVmZTA1ZTEzNjk1ZjM1NjliZDEwNjhjZDdhN2E4NTlhZjA1M2M3MmFiNGRlN2I5NjU1MiIsInZlcnNpb24iOjF9.qjSNWo5Z1_b29Rw5kH2ffWSsDx9KVVZHJ8JUyZdQ7LEDQyOvi8RIzN6ich_FlfFLmw4MuZUKOhbk5Pxocm33AA | |
| <!-- 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 | |