Jordan_Name_Disambiguation

This model is a fine-tuned version of distilbert-base-uncased used for token classifcation. It achieves the following results on the evaluation set:

• Loss: 0.0025
• Precision: 0.9811
• Recall: 0.9811
• F1: 0.9811
• Accuracy: 0.9995

Model Description

This model is used to differentiate a mention of the country "jordan" as a place the veteran served vs the mention of "jordan" as a name, "jordan" as another location i.e. West Jordan Utah, or the country "jordan" as part of form language.

Intended uses & limitations

This is only intended to be used to determine if "jordan" is in the context of a service location.

This model was trained on a limited amount of data for a narrow classification task.

Training Data

The training data has two columns "text" and "service_location". The text column contains a snippet of text containing the word "Jordan" in various contexts. The service_location column indicates if the mention of the word "Jordan" is referencing a service location (1) or not (0).

NOTE: The training data has PII and is only accessible to team members on S3.

Data Analysis

The chart below displays the distribution of examples containing a "jordan" service token

Training Procedure

Preprocessing

The data went through the following preprocessing steps:

• Tokenize text into words
• Create NER tags for "jordan" i.e. 0's and 1's
• Apply distilbert-base-uncased tokenizer and align tokens to labeling scheme ["O", "B-SER", "I-SER"]

Training Hyperparameters

• learning_rate: 5e-05
• train_batch_size: 24
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training Results

Framework Versions

• Transformers 4.33.2
• Pytorch 2.0.1+cpu
• Datasets 2.14.5
• Tokenizers 0.13.3