datalawyer / segmenter

test_crf

This model is a fine-tuned version of BAAI/bge-m3-retromae on the adalbertojunior/segmentacao dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.0063
• eval_model_preparation_time: 0.0032
• eval_precision: 0.6294
• eval_recall: 0.6832
• eval_f1: 0.6552
• eval_accuracy: 0.9989
• eval_runtime: 12.8447
• eval_samples_per_second: 3.893
• eval_steps_per_second: 3.893
• step: 0

Usage

from transformers import pipeline
segmenter = pipeline("ner", model="./models/test_crf_v2", aggregation_strategy="simple", device=0)
entities = segmenter(text)

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1.0

Framework versions

• Transformers 4.43.4
• Pytorch 2.4.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1