Instructions to use denizspynk/req_mod_ner_modelv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denizspynk/req_mod_ner_modelv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="denizspynk/req_mod_ner_modelv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("denizspynk/req_mod_ner_modelv2") model = AutoModelForTokenClassification.from_pretrained("denizspynk/req_mod_ner_modelv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Please contact me via [LinkedIn](https://www.linkedin.com/in/denizayhan/) if you have any questions about this model or the dataset used.
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The dataset and this model were created as part of the final project assignment of the Natural Language Understanding course (XCS224U) from Professional AI Program of the Stanford School of Engineering.
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The model achieves the following results on the evaluation set:
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- Loss: 0.6791
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Please contact me via [LinkedIn](https://www.linkedin.com/in/denizayhan/) if you have any questions about this model or the dataset used.
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The dataset and this model were created as part of the final project assignment of the Natural Language Understanding course (XCS224U) from the Professional AI Program of the Stanford School of Engineering.
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The model achieves the following results on the evaluation set:
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- Loss: 0.6791
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