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