Rename README.md to Rhttps://huggingface.co/azherali/distilbert-imdb_mask_modelEADME.md

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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Rhttps:/huggingface.co/azherali/distilbert-imdb_mask_modelEADME.md

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+---
+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.
+
+---
+
+## ✨ 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
+
+---
+
+## 🚀 Quickstart
+
+### Use with `pipeline` (Fill-Mask)
+
+```python
+from transformers import pipeline
+
+nlp = pipeline("fill-mask", model="azherali/distilbert-imdb_mask_model")
+
+nlp("This movie was absolutely [MASK] and the performances were stunning.")
+# [{'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)
+
+
+```python
+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  
+
+---
+
+## ⚡ 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  
+
+---