Update README.md

README.md

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----
-license: mit
----
+---
+base_model: microsoft/Phi-3.5-mini-instruct
+language:
+- en
+library_name: transformers
+license: mit
+license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- nlp
+- ner
+---
+
+## Geo-Temporal Entity Recognition Model
+
+This model is a finetuned version of [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct). It detects the location and date entities in a query. It also maps the location entities to the corresponding set of countries, and the date entities to either a start and end date, or the corresponding months. Additionally, it generates the query that is cleaned from the date entities and the location entities that are countries.
+
+The prompt on which the model was finetuned is the following: 
+
+```python
+from datetime import date
+today = date.today()
+
+schema = """
+country: Extracted list of country or countries,
+periodStart: Period start in ISO 8601 format, filled in only if date-related entity corresponds to absolute date range, else null,
+periodEnd: Period end in ISO 8601 format, filled in only if date-related entity corresponds to absolute date range, else null,
+phase: A list of months indicated by integers from 1 to 12, filled in only if date-related entity corresponds to a reccuring yearly period. It should be null in case `periodStart` or `periodEnd` has been extracted.,
+location: A list of the detected location-related entities,
+date: A list of date-related entities,
+cleanedQuery: A list of parts of the query cleaned from the extracted date-related entity and the location-related entity and their related parts (e.g. prepositions)
+"""
+
+EXTENDED_INSTRUCTION_TEXT = """
+You are a Name-Entity Recognition system specialized in extracting and processing location and date related entities from text. Follow these steps:
+
+1. Extract exact entities from the text:
+   - Location entities: Extract only if they are specific place names (not general terms like "sample locations")
+   - Date entities: Extract dates exactly as they appear in the text
+   Both should be extracted exactly as mentioned in the text, without modifications.
+
+2. For each detected location entity:
+   - Map it to corresponding country name(s)
+   - If the location itself is a country, include it in the country list
+   - If country cannot be determined, return an empty list
+
+3. For date-related entities, classify them into one of two categories:
+   a) Absolute date range:
+      - Convert to ISO 8601 date format (YYYY-MM-DD)
+      - Set periodStart and periodEnd
+      - Set phase to null
+      - Use %(today)s as reference for relative dates
+   
+   b) Recurring yearly period:
+      - Set phase as list of integers (1-12) representing months
+      - Set periodStart and periodEnd to null
+
+4. Clean the query by removing:
+   - Detected date entities and their syntactic relations (e.g., prepositions)
+   - Location entities (only if they are countries) and their relations
+   Return the remaining parts as a list of strings
+
+Return the results in JSON format matching this schema: %(schema)s
+
+IMPORTANT:
+- Always return all fields defined in the schema
+- Return only the JSON without any additional explanation or notes
+- Ensure the JSON is properly formatted and parsable
+""" % {"today": today, "schema": schema}
+```