deberta-v3-base-zyda-2-sentiment

This model is a fine-tuned version of agentlans/deberta-v3-base-zyda-2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0408
• Mse: 0.0408

Model description

More information needed

Intended uses & limitations

Example use:

import torch
import numpy as np
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load model and tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = "agentlans/deberta-v3-base-zyda-2-sentiment"
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=1).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to perform inference
def predict_score(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    with torch.no_grad():
        logits = model(**inputs).logits
    return logits.item()

# Example usage
input_text = "I accidentally the whole thing. Is that bad?"
score = predict_score(input_text)
print(f"Predicted score: {score}")

Example output:

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: 64
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3