Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- dslim/distilbert-NER
+tags:
+- email
+- org
+---
+# Zefty/distilbert-ner-email-org
+
+distilbert-ner-email-org is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on a set of [job application emails](https://www.kaggle.com/datasets/rasho330/job-application-email-anonymized-and-feature-rich).
+The model is fine-tuned specifically to identify the organizations (ORG) entity, thus it CANNOT identify location (LOC), person (PER), and Miscellaneous (MISC), which is available in the original model. 
+This model is fine-tuned specifically to identify the organizations for a personal side-project of mine to extract out companies from job application emails. 
+
+
+# How to use
+This model can be utilized with the Transformers pipeline for NER.
+
+```
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("Zefty/distilbert-ner-email-org")
+model = AutoModelForTokenClassification.from_pretrained("Zefty/distilbert-ner-email-org")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
+example = "Thank you for Applying to Amazon!"
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+# Training data
+This model was fine-tuned on a set of [job application emails](https://www.kaggle.com/datasets/rasho330/job-application-email-anonymized-and-feature-rich).
+Instead of using the full tokens from the CoNLL-2003 English Dataset, this dataset only includes the ORG token.
+
+|Abbreviation|Description|
+| -------- | ------- |
+|O|Outside of a named entity|
+|B-ORG|Beginning of an organization right after another organization|
+|I-ORG|organization|
+
+# Eval results
+|Metric|Score|
+| -------- | ------- |
+|Loss|0.0898725837469101|
+|Precision|0.7111111111111111|
+|Recall|0.8205128205128205|
+|F1|0.7619047619047619|
+|Accuracy|0.9760986309658876|