đ Deberta-v3-large_boolq
This model is a fine - tuned version of microsoft/deberta-v3-large on the boolq dataset. It can be used for text classification tasks, achieving high accuracy on the boolq dataset.
đ Quick Start
This model is a fine - tuned version of microsoft/deberta-v3-large on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4601
- Accuracy: 0.8835
⨠Features
- Fine - tuned on the boolq dataset, suitable for text classification tasks.
- Achieves high accuracy on the evaluation set.
đĻ Installation
No specific installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
Basic Usage
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("nfliu/deberta-v3-large_boolq")
tokenizer = AutoTokenizer.from_pretrained("nfliu/deberta-v3-large_boolq")
examples = [
("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."),
("Water is wet", "Contrary to popular belief, water is not wet.")
]
encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
model_output = model(**encoded_input)
probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist()
probability_no = [round(prob[0], 2) for prob in probabilities]
probability_yes = [round(prob[1], 2) for prob in probabilities]
for example, p_no, p_yes in zip(examples, probability_no, probability_yes):
print(f"Question: {example[0]}")
print(f"Context: {example[1]}")
print(f"p(No | question, context): {p_no}")
print(f"p(Yes | question, context): {p_yes}")
print()
đ Documentation
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: 1e - 05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
No log |
0.85 |
250 |
0.5306 |
0.8823 |
0.1151 |
1.69 |
500 |
0.4601 |
0.8835 |
0.1151 |
2.54 |
750 |
0.5897 |
0.8792 |
0.0656 |
3.39 |
1000 |
0.6477 |
0.8804 |
0.0656 |
4.24 |
1250 |
0.6847 |
0.8838 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
đ License
This model is licensed under the MIT license.
đ Model Information
Property |
Details |
Model Type |
Fine - tuned version of microsoft/deberta - v3 - large on boolq dataset |
Training Data |
boolq |
Metrics |
Accuracy: 0.8834862385321101 |