Update README.md

README.md

@@ -1,29 +1,60 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- github
+datasets:
+- lewtun/github-issues
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
-
+This is a fine-tuned `bert-base-uncased` model for multi-label classification of GitHub issues into various tags (e.g., `bug`, `enhancement`, `documentation`, etc.).
 
 ## Model Details
 
+- **Base model**: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
+- **Task**: Multi-label Text Classification
+- **Labels**: 19 possible tags (e.g., `bug`, `dataset request`, `help wanted`, etc.)
+- **Tokenizer**: `bert-base-uncased`
+  
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model performs multi-label classification of GitHub issues based on their content. Each issue is represented by a combination of its title, body, state, and associated comments. These components are concatenated into a single input string using the following format:
+```python
+  if example.get("title"):
+          text_parts.append("Title: " + example["title"])
+      if example.get("body"):
+          text_parts.append("Body: " + example["body"])
+      if example.get("state"):
+          text_parts.append("State: " + example["state"])
+  
+      comments = example.get("comments", [])
+      if comments:
+          text_parts.append("Comments: " + " ".join(comments))
+  
+      return {"text": " \n ".join(text_parts)}
+```
+
+The resulting "text" field serves as the input to the model. Each text entry is tokenized using the Hugging Face bert-base-uncased tokenizer with the following configuration:
+```python
+  tokenizer(
+        example["text"],
+        padding="max_length",
+        truncation=True,
+        max_length=512
+  )
+```
+The target labels are constructed as a binary vector of length 19, where each element corresponds to one of the predefined GitHub
+issue tags (e.g., bug, enhancement, documentation, etc.). Each element in the vector is set to 1 if the tag is present for the issue, and 0 otherwise. This format enables the model to perform multi-label classification, allowing it to assign multiple relevant tags to a single GitHub issue.
+
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
 ### Model Sources [optional]
 
@@ -71,6 +102,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
+
 [More Information Needed]
 
 ## Training Details