README.md

metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
pipeline_tag: fill-mask
tags:
  - masked-language-modeling
  - fill-mask
  - distilbert
  - imdb
  - domain-adaptation
  - nlp
  - transformers
model-index:
  - name: distilbert-imdb_mask_model
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: IMDB Movie Reviews (unsupervised text)
          type: imdb
          split: train
        metrics:
          - name: Loss
            type: loss
            value: N/A
          - name: Perplexity
            type: perplexity
            value: N/A

Masked Language Modeling

📌 Model Overview

This model is a fine-tuned version of distilbert-base-uncased on the IMDb dataset using the Masked Language Modeling (MLM) objective.
It is designed for domain adaptation, helping DistilBERT better understand the linguistic style of IMDb movie reviews.

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✨ What this model does

• Learns to predict masked tokens in movie-review text (MLM / fill-mask).
• Helpful as a domain-adapted backbone for:
  • Sentiment analysis on reviews
  • Topic classification / intent
  • Review-specific QA / RAG preprocessing
  • Any task that benefits from in-domain representations

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🚀 Quickstart

Use with pipeline (Fill-Mask)

from transformers import pipeline

pipe = pipeline("fill-mask", model="azherali/distilbert-imdb_mask_model")

text = "This movie was absolutely [MASK] and the performances were stunning."
pipe(text)
# [{'sequence': 'this movie was absolutely fantastic ...', 'score': ...}, ...]

for x in pipe(text):
  print(x["sequence"])

output:
# this movie was absolutely fantastic and the performances were stunning.
# this movie was absolutely stunning and the performances were stunning.
# this movie was absolutely beautiful and the performances were stunning.
# this movie was absolutely brilliant and the performances were stunning.
# this movie was absolutely wonderful and the performances were stunning.

Use with AutoModel (programmatic logits)

import torch
from transformers import AutoModelForMaskedLM,AutoTokenizer

model_checkpoint = "azherali/distilbert-imdb_mask_model"

model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

text ="This movie was absolutely [MASK] and the performances were stunning."

inputs = tokenizer(text, return_tensors="pt")
token_logits = model(**inputs).logits
# Find the location of [MASK] and extract its logits
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
mask_token_logits = token_logits[0, mask_token_index, :]
# Pick the [MASK] candidates with the highest logits
top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()

for token in top_5_tokens:
    print(f"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'")

📈 Training Results

The model was trained for 5 epochs on the IMDb dataset using the Masked Language Modeling (MLM) objective.

Loss Progression:

Epoch	Training Loss	Validation Loss	Perplexity
1	2.5249	2.3440	10.42
2	2.3985	2.2913	9.89
3	2.3441	2.2569	9.55
4	2.3079	2.2328	9.33
5	2.2869	2.2271	9.27

✔️ Final Training Loss: 2.28
✔️ Final Validation Loss: 2.22
✔️ Final Perplexity: 9.27

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⚡ Training Configuration

• Model: distilbert-base-uncased
• Dataset: IMDb (unsupervised)
• Epochs: 5
• Batch Size: 32
• Optimizer: AdamW
• Learning Rate Scheduler: Linear warmup + decay
• Total Steps: 9,580
• Total FLOPs: 1.02e+16

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