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cassandra-themis/
lsg-ner-phrases-16384

Name: lsg-ner-phrases-16384
Availability: InStock

generated_from_trainer

custom_code

cassandra-themis/ner-phrases

autotrain_compatible

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lsg-ner-phrases-16384

This model is a fine-tuned version of lsg-base-16384-juri on the cassandra-themis/ner-phrases dataset. It achieves the following results on the evaluation set:

Loss: 0.0058
New Sentence Precision: 0.9955
New Sentence Recall: 0.9932
New Sentence F1: 0.9943
New Sentence Number: 442
Overall Precision: 0.9955
Overall Recall: 0.9932
Overall F1: 0.9943
Overall Accuracy: 0.9996

Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
import re

model_path = "cassandra-themis/lsg-ner-phrases-16384"

model = AutoModelForTokenClassification.from_pretrained(model_path, trust_remote_code=True, use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=True)
ner_pipe = pipeline("token-classification", model=model, tokenizer=tokenizer)


document = "My document"
document_flattened = re.sub(r'(\s|\t|\n)+', r' ', document).strip()

prediction = ner_pipe(document_flattened, aggregation_strategy="simple")

sentences = []
for i in range(len(prediction) - 1):
    sentences.append(document_flattened[prediction[i]["start"]:prediction[i+1]["start"]].strip())
print("\n".join(sentences))

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: 8e-05
train_batch_size: 2
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 150.0

Training results

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.11.6