bert_clf_results

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

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

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