bert english uncased finetuned pos

Introduction

The BERT-ENGLISH-UNCASED-FINETUNED-POS model is designed for token classification tasks, specifically fine-tuned for Part-of-Speech (POS) tagging. It is built on the BERT architecture, utilizing the English uncased variant.

Architecture

This model employs the BERT architecture, which is known for its transformer-based framework. It supports libraries such as PyTorch and JAX, and is compatible with Safetensors for efficient storage. The model can also be deployed using inference endpoints.

Training

The model has been fine-tuned with specific attention to Part-of-Speech tagging. The following are some of the POS tags used in this model:

• ADP: Adpositions like "in", "on".
• ADJ: Adjectives like "significant", "global".
• ADV: Adverbs like "quickly", "often".
• AUX: Auxiliary verbs like "is", "was".
• CCONJ: Coordinating conjunctions like "and", "but".
• DET: Determiners like "the", "a".
• INTJ: Interjections like "oh", "wow".
• NOUN: Nouns like "man", "city".
• NUM: Numbers like "one", "2022".
• PART: Particles like "'s", "to".
• PRON: Pronouns like "he", "which".
• PROPN: Proper nouns like "Neil Armstrong", "Paris".
• PUNCT: Punctuation marks like ",", ".".
• SCONJ: Subordinating conjunctions like "because", "although".
• SYM: Symbols like "$", "%".
• VERB: Verbs like "run", "is".
• X: Other words that do not fit into the above categories.

Guide: Running Locally

To run the model locally:

1. Setup Environment: Ensure you have Python and the necessary libraries installed. It's recommended to use a virtual environment.
2. Install Transformers: Use pip to install the Hugging Face Transformers library.

   pip install transformers
   

3. Load Model: Use the Transformers library to load the model.

   from transformers import AutoTokenizer, AutoModelForTokenClassification
   
   tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
   model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
   

4. Inference: Tokenize input text and perform inference.
5. Cloud GPUs: For optimal performance, especially with large datasets, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The model is provided under the license specific to the repository on Hugging Face. Users should refer to the repository's license file for detailed terms and conditions.