t5-english-ner

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T5-Encoder(T5-large model) fine-tuned on very small dataset for token classification

Simple experimental model that was trained in 3 epochs on very small dataset

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification, NerPipeline

model = AutoModelForTokenClassification.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)

pipe = NerPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
print(pipe("London is the capital city of England and the United Kingdom"))
"""
[{'entity_group': 'LOCATION',
  'score': 0.84536326,
  'word': 'London',
  'start': 0,
  'end': 6},
 {'entity_group': 'LOCATION',
  'score': 0.8957489,
  'word': 'England',
  'start': 30,
  'end': 37},
 {'entity_group': 'LOCATION',
  'score': 0.73186326,
  'word': 'UnitedKingdom',
  'start': 46,
  'end': 60}]
"""

Usage in spacy

pip install spacy transformers git+https://github.com/explosion/spacy-huggingface-pipelines -q

import spacy
from spacy import displacy

text = "My name is Sarah and I live in London"

nlp = spacy.blank("en")
nlp.add_pipe("hf_token_pipe", config={"model": "imvladikon/t5-english-ner", "kwargs": {"trust_remote_code":True}})
doc = nlp(text)
print(doc.ents)
# (Sarah, London)

This model is a fine-tuned version of t5-large on the private(en) dataset. It achieves the following results on the evaluation set:

• Loss: 0.1956
• Commercial Item Precision: 0.0
• Commercial Item Recall: 0.0
• Commercial Item F1: 0.0
• Commercial Item Number: 1
• Date Precision: 0.8125
• Date Recall: 0.9286
• Date F1: 0.8667
• Date Number: 14
• Location Precision: 0.7143
• Location Recall: 0.75
• Location F1: 0.7317
• Location Number: 20
• Organization Precision: 0.8588
• Organization Recall: 0.9125
• Organization F1: 0.8848
• Organization Number: 80
• Other Precision: 0.3684
• Other Recall: 0.3333
• Other F1: 0.35
• Other Number: 21
• Person Precision: 0.8182
• Person Recall: 0.9310
• Person F1: 0.8710
• Person Number: 29
• Quantity Precision: 0.8
• Quantity Recall: 0.8571
• Quantity F1: 0.8276
• Quantity Number: 14
• Title Precision: 0.0
• Title Recall: 0.0
• Title F1: 0.0
• Title Number: 7
• Overall Precision: 0.75
• Overall Recall: 0.7903
• Overall F1: 0.7696
• Overall Accuracy: 0.9534

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1

WANDB

training logs and reports