Text Classification
Transformers
PyTorch
TensorBoard
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sgugger/glue-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("sgugger/glue-mrpc")Quick Links
glue-mrpc
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.6566
- Accuracy: 0.8554
- F1: 0.8974
- Combined Score: 0.8764
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: 3.0
Training results
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
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Dataset used to train sgugger/glue-mrpc
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Evaluation results
- Accuracy on GLUE MRPCself-reported0.855
- F1 on GLUE MRPCself-reported0.897
- Accuracy on gluevalidation set self-reported0.855
- Precision on gluevalidation set self-reported0.872
- Recall on gluevalidation set self-reported0.925
- AUC on gluevalidation set self-reported0.905
- F1 on gluevalidation set self-reported0.897
- loss on gluevalidation set self-reported0.656
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/glue-mrpc")