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## Model description
# entity-recognition-general-sota-v1-finetuned-ner

This model is a fine-tuned version of [numind/entity-recognition-general-sota-v1](https://huggingface.co/numind/entity-recognition-general-sota-v1) on Babelscape/MultiNerd dataset.

It achieves the following results on the validation set:
- Loss: 0.0396
- Precision: 0.9138
- Recall: 0.9146
- F1: 0.9142
- Accuracy: 0.9857


## 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.
Training data - 131280 items
Validation data - 16410 items

Trained on all tags from the MultiNERD dataset.

labels2ids = {
    "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-BIO": 9,
    "I-BIO": 10,
    "B-CEL": 11,
    "I-CEL": 12,
    "B-DIS": 13,
    "I-DIS": 14,
    "B-EVE": 15,
    "I-EVE": 16,
    "B-FOOD": 17,
    "I-FOOD": 18,
    "B-INST": 19,
    "I-INST": 20,
    "B-MEDIA": 21,
    "I-MEDIA": 22,
    "B-MYTH": 23,
    "I-MYTH": 24,
    "B-PLANT": 25,
    "I-PLANT": 26,
    "B-TIME": 27,
    "I-TIME": 28,
    "B-VEHI": 29,
    "I-VEHI": 30,
  }

## Training procedure

HF Trainer module

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 20
- eval_batch_size: 20
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0323        | 1.0   | 6564 | 0.0396          | 0.9138    | 0.9146 | 0.9142 | 0.9857   |

Evaluation on test set:
{'eval_loss': 0.02707073651254177,
 'eval_precision': 0.9378337879893957,
 'eval_recall': 0.9518034704620784,
 'eval_f1': 0.9447669917943954,
 'eval_accuracy': 0.9901678553418342,
 'eval_runtime': 133.0665,
 'eval_samples_per_second': 247.305,
 'eval_steps_per_second': 30.917}

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["Babelscape/multinerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "numind/entity-recognition-general-sota-v1", "pipeline_tag": "token-classification", "model-index": [{"name": "entity-recognition-general-sota-v1-finetuned-ner", "results": []}]}
transformers tensorboard safetensors roberta token-classification generated_from_trainer en dataset:Babelscape/multinerd base_model:numind/entity-recognition-general-sota-v1 license:mit autotrain_compatible endpoints_compatible region:us