bert_clf_results

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9611
• Accuracy: 0.7011

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: 32
• 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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0767	1.0	5401	0.8447	0.7087
0.6523	2.0	10803	0.8287	0.7156
0.7209	3.0	16204	0.8852	0.7121
0.4274	4.0	21604	0.9611	0.7011

Code Implementation

from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Andyrasika/bert_clf_results")
inputs = tokenizer(prompt, return_tensors="pt")


model = AutoModelForSequenceClassification.from_pretrained("Andyrasika/bert_clf_results")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

Output

'LABEL_4'

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu118
• Datasets 2.16.0
• Tokenizers 0.15.0