Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/binary-classification-not-evaluated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/binary-classification-not-evaluated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/binary-classification-not-evaluated")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/binary-classification-not-evaluated") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/binary-classification-not-evaluated", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| model-index: | |
| - name: autoevaluate/binary-classification-not-evaluated | |
| results: | |
| - 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.eyJoYXNoIjoiZTFhNTM5OGFkNTYxNmM5OTRmNmI0MWU1MWFiYzM5ODM0MTdiYmZmYmExOTI5ZTQzNGQ0YWRlNjQ2MjdjOWFhYSIsInZlcnNpb24iOjF9.fcoYl-t_iYhGKGJqLB-AGrmAsd_QkUXWJFsxdi-x6RjTJeCevEHSRABdLKM2UM7yJF8nGwvWjI68r1fJ1OlSCw | |
| - type: precision | |
| value: 0.8898678414096917 | |
| name: Precision | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2FjZWMyMzUzYjI5MDdkN2M3OGZhMzU0YmRlMDQwZjc4ZWU4ZTljYWFjZDVkMzRkMTBiOGM4YmQyMjM0YTUyOCIsInZlcnNpb24iOjF9.7d28G0boU5Xc-3-ox3040mluwIbls0pjLG8XROJaqkG6ei0HVKyTds1fzgr3-JZxK6wylItVGDPg0Z5MAa5yAA | |
| - type: recall | |
| value: 0.9099099099099099 | |
| name: Recall | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDhhZThhZjE2YzliYTAxODQ4NDNiYTM4OGQxOGQ0NzU3YzljZjViNDEyODgwNjg3NGFkZDU3MGVjNDM5ZmE2MyIsInZlcnNpb24iOjF9.eMy2JTxw821ff8umlAyX20SGSlll2e2yaVaEab3gl5xwU36qocNBve_IfluAox4J5bg8VCKhRdR-yzhJ01IZAw | |
| - type: auc | |
| value: 0.9672186789593331 | |
| name: AUC | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2U5YTRmM2FjNDdmYmMzY2U0MzliMWYxOGYwMGYyNTJkMzk2YjRhMWJhMTAzMDU1NmUyNjEzYjA1NTBiNmNlMSIsInZlcnNpb24iOjF9.iWtm0L1Fvfrh5S4DEkZCx2ewFajs26DpFbX8YAOay_dkFdpgJGbr6avAyKg-tUXjUGpinW_DpeGnluXF-MtQAw | |
| - type: f1 | |
| value: 0.8997772828507795 | |
| name: F1 | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgwMGM1NzMzZTBkMTA2YjAyYjhkZGYzZWQxZDY4ZjIzNmZkY2U1Mjk0NGZkOGVkN2QxZmMzMjdkNWIzOWYwZiIsInZlcnNpb24iOjF9.MT-ofNgyx-zxqwBjbzW5oeFG0YOAcN9OZQNpbJSvGZDWRi6ZWd5hrWohAEviNHA12LQsdu4s5oRgPpWPe25kAA | |
| - type: loss | |
| value: 0.30092036724090576 | |
| name: loss | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2ZjY2FjM2M1MmM2ZmRjYmVhMGY2YTgxZjhhMTFlNjY3OTg0MzUzZjYzZWMxZTAxNTc5MjhkMDY0NzhkYTBkNSIsInZlcnNpb24iOjF9.2JQmUWcTR6_8dsFeBKt_UG0dg-qJFIIoDFxYx2O059ikdIBKHu5DqY0U2aJvuyTyWxzKxOxkSStzRSZEKOf-Bw | |
| - type: matthews_correlation | |
| value: 0.793630584795814 | |
| name: matthews_correlation | |
| verified: true | |
| verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjZkY2IzNmMwNGY0N2NiNGQ4MGI2Yzk3YTY1M2ExZjBmYTIyMGM1YzA4NzRiMWY0YTZlOTY2YmY4NWMxYTliNSIsInZlcnNpb24iOjF9.c7TFOc93GiblJ49JbsWknmj0yPFAvO50eep4Dcof8aKbysNxDuprg67CdWN7WqIU3cEFgIcRPyC6nX5t44fHDg | |
| <!-- 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 | |