# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nadellaroshni/test_model2")
model = AutoModelForSequenceClassification.from_pretrained("nadellaroshni/test_model2")Quick Links
test_model2
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3250
- Accuracy: 0.863
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5093 | 1.0 | 625 | 0.3441 | 0.852 |
| 0.3595 | 2.0 | 1250 | 0.3250 | 0.863 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.0
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Model tree for nadellaroshni/test_model2
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadellaroshni/test_model2")