Update README.md

README.md

@@ -20,39 +20,11 @@ base_model:
 - **Knowledge Base Construction:** Build and reference new knowledge bases using the model's strong generalization capabilities
 
 ### Recommended Preprocessing
-- Use `[ENT]` tokens to mark entity mentions: `[ENT] mention [ENT]`
+- Use `[ENT]` tokens to mark entity mentions: `left context [ENT] mention [ENT] right context`
 - Consider using NER models to identify candidate mentions
 - For non-standard entities (e.g., "daytime"), extract noun phrases using NLTK or spaCy
 - Clean and filter knowledge base entries to remove irrelevant concepts
 
-## Model Details
-
-
-### Training Data
-- **Dataset:** 3 million pairs of Wikipedia anchor text links and Wikipedia page descriptions
-- **Source:** Wikipedia anchor links paired with first few hundred words of target pages
-- **Special Token:** `[ENT]` token added to mark entity mentions
-- **Max Sequence Length:** 256 tokens (both mentions and descriptions)
-
-### Training Details
-- **Hardware:** Single 80GB H100 GPU
-- **Batch Size:** 80
-- **Learning Rate:** 1e-5 with cosine scheduler  
-- **Loss Function:** Batch hard triplet loss (margin=0.4)
-- **Inspiration:** Meta AI's BLINK and Google's "Learning Dense Representations for Entity Retrieval"
-
-## Performance
-
-### Benchmark Results
-- **Dataset:** Zero-Shot Entity Linking (Logeswaran et al., 2019)
-- **Metric:** Recall@64
-- **Score:** 80.29%
-- **Comparison:** Meta AI's BLINK achieves 82.06% on the same test set - slightly higher than ours, however, their model was trained on the training set but ours was not.
-- **Conclusion:** Our model has strong zero-shot performance
-
-### Usage Recommendations
-- **Similarity Threshold:** 0.7 for positive matches (based on empirical testing)
-
 ## Code Example
 
 ```python
@@ -132,34 +104,40 @@ for i, definition in enumerate(definitions):
     print(f"Similarity: {sim_value:.4f}\n")
 ```
 
-## Input Format
+## Model Details
 
-### Mention Context
-- Mark target mentions with `[ENT]` tokens: `"Text with [ENT] entity mention [ENT] in context"`
-- Maximum length: 256 tokens
 
-### Entity Descriptions  
-- Provide entity descriptions (e.g., Wikipedia abstracts)
-- Maximum length: 256 tokens
+### Training Data
+- **Dataset:** 3 million pairs of Wikipedia anchor text links and Wikipedia page descriptions
+- **Source:** Wikipedia anchor links paired with first few hundred words of target pages
+- **Special Token:** `[ENT]` token added to mark entity mentions
+- **Max Sequence Length:** 256 tokens (both mentions and descriptions)
 
-## Limitations and Biases
+### Training Details
+- **Hardware:** Single 80GB H100 GPU
+- **Batch Size:** 80
+- **Learning Rate:** 1e-5 with cosine scheduler  
+- **Loss Function:** Batch hard triplet loss (margin=0.4)
+- **Inspiration:** Meta AI's BLINK and Google's "Learning Dense Representations for Entity Retrieval"
+
+## Performance
 
-- **Language:** English only
-- **Domain:** Primarily trained on Wikipedia data
-- **Bias:** May inherit biases present in Wikipedia content
-- **Performance:** Slightly lower than supervised models on in-domain tasks
+### Benchmark Results
+- **Dataset:** Zero-Shot Entity Linking (Logeswaran et al., 2019)
+- **Metric:** Recall@64
+- **Score:** 80.29%
+- **Comparison:** Meta AI's BLINK achieves 82.06% on the same test set - slightly higher than ours, however, their model was trained on the training set but ours was not.
+- **Conclusion:** Our model has strong zero-shot performance
 
-## References
+### Usage Recommendations
+- **Similarity Threshold:** 0.7 for positive matches (based on empirical testing)
 
-- Logeswaran et al. (2019). [Zero-shot Entity Linking with Efficient Long Range Sequence Modeling](https://arxiv.org/pdf/1906.07348)
-- Meta AI BLINK: [GitHub Repository](https://github.com/facebookresearch/BLINK)
-- Google's Learning Dense Representations for Entity Retrieval
 
 ## Citation
 
 ```bibtex
 @misc{roberta-large-entity-linking,
-  author = {[Your Name/Organization]},
+  author = {[Glass, Lewis & Co.]},
   title = {RoBERTa Large Entity Linking},
   year = {2024},
   publisher = {Hugging Face},