abidlabs/ml-intern-api-imdb-distilbert-20260613-020123

ML Intern API IMDb DistilBERT Example

This model was produced by a public ML Intern API example run.

• —Base model: distilbert-base-uncased
• —Dataset: imdb
• —Train subset: 1,000 examples
• —Eval subset: 200 examples
• —Training: 60 Trainer steps, batch size 16, learning rate 5e-5
• —Eval metrics: {"epoch": 0.9523809523809523, "eval_accuracy": 0.815, "eval_loss": 0.4811082184314728, "eval_runtime": 24.2035, "eval_samples_per_second": 8.263, "eval_steps_per_second": 0.289}

<!-- ml-intern-provenance -->

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• —Try ML Intern: https://smolagents-ml-intern.hf.space
• —Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'abidlabs/ml-intern-api-imdb-distilbert-20260613-020123'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.