Text Classification
Transformers
PyTorch
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Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-rte")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-rte")Quick Links
bert-base-uncased-rte
This model is a fine-tuned version of bert-base-uncased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.6972
- Accuracy: 0.6895
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 156 | 0.6537 | 0.6318 |
| No log | 2.0 | 312 | 0.6383 | 0.6534 |
| No log | 3.0 | 468 | 0.6972 | 0.6895 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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Model tree for JeremiahZ/bert-base-uncased-rte
Base model
google-bert/bert-base-uncasedDataset used to train JeremiahZ/bert-base-uncased-rte
Evaluation results
- Accuracy on GLUE RTEself-reported0.690
- Accuracy on gluevalidation set verified0.682
- Precision on gluevalidation set verified0.705
- Recall on gluevalidation set verified0.565
- AUC on gluevalidation set verified0.739
- F1 on gluevalidation set verified0.627
- loss on gluevalidation set verified0.700
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-rte")