license: mit
base_model: numind/entity-recognition-general-sota-v1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: entity-recognition-general-sota-v1-finetuned-ner-X
results: []
datasets:
- Babelscape/multinerd
language:
- en
library_name: transformers
pipeline_tag: token-classification
Model description
entity-recognition-general-sota-v1-finetuned-ner-X
This model is a fine-tuned version of numind/entity-recognition-general-sota-v1 on an Babelscape/MultiNerd dataset.
It achieves the following results on the validation set:
- Loss: 0.0228
- Precision: 0.9472
- Recall: 0.9621
- F1: 0.9546
- Accuracy: 0.9915
Training and evaluation data
The dataset if filtered on english language and sampled first 1M on train and 100k on validation. further filtered with data containing atleast one tag from labels2ids mentioned below. Train data - 110723 items Validation data - 13126 items
Trained on below listed tags from the MultiNERD dataset.
labels2ids_B = { "O": 0, "B-PER": 1, "I-PER": 2, "B-ORG": 3, "I-ORG": 4, "B-LOC": 5, "I-LOC": 6, "B-ANIM": 7, "I-ANIM": 8, "B-DIS": 9, "I-DIS": 10 }
Training procedure
HF Trainer module
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 35
- eval_batch_size: 35
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training & Test set evaluation results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0214 | 1.0 | 3164 | 0.0228 | 0.9472 | 0.9621 | 0.9546 | 0.9915 |
Test set Evaluation results: { 'eval_loss': 0.017866812646389008, 'eval_precision': 0.9557654500384648, 'eval_recall': 0.9739558381603589, 'eval_accuracy': 0.9931328078645237, 'eval_runtime': 109.6919, 'eval_samples_per_second': 269.045, 'eval_steps_per_second': 33.631 }
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0