--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 - autoevaluate/conll2003-sample metrics: - precision - recall - f1 - accuracy model-index: - name: entity-extraction results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - type: precision value: 0.8862817854414493 name: Precision - type: recall value: 0.9084908826490659 name: Recall - type: f1 value: 0.8972489227709645 name: F1 - type: accuracy value: 0.9774889986814304 name: Accuracy - task: type: token-classification name: Token Classification dataset: name: autoevaluate/conll2003-sample type: autoevaluate/conll2003-sample config: autoevaluate--conll2003-sample split: test metrics: - type: accuracy value: 0.9680247550283652 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTgzYzIwNTcyNzgxN2JiNGU0Y2RhMmY2YzRhMzUyNGY5NGE2MDA0NTVmYTFjYzdjMWQ2M2UxOTY4YmJkNWI2OCIsInZlcnNpb24iOjF9.TXZVtZoAvkUw_iXjmVwAdPtzhimwv33pA0BqxbKLGP3QSpJAsFbAbDwh2kUaKH4mTtgmcGgmtsywIgV5_ZEFAA - type: precision value: 0.9708377518557795 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWJkYzQ0MzhmNGE4Y2UyMmIzNThmMTdlZjMzODdlOWMzMTg1NTEwNWQ3NDMyNTYxODZiMzZhYTQ5NDU2ZGZlMSIsInZlcnNpb24iOjF9.rFvd0bxUagfktMsv-Q0NJr2WN2MuZ74dR0Opq9_MqjXnhi1wPxRcfbjw2RYUKnRM9PVVkBrb3WyTGYljcJYMCA - type: recall value: 0.9754928076718167 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjkzMGExNzU3NWY4Y2E0ODgyZTU5MzY1NTYxMDU3M2E3N2RkMmEwNzRmNWRmZDA1N2Y3MDQ5OGE3ZWQ3ZDA0NyIsInZlcnNpb24iOjF9.yAlh4o8i2o4GG6TES8-IoYlvqCh8NS09OeQ8yILRiRo8Uk9u6CdaZAklstD60jyMlanP7c_IP-SQsqokJ41tCg - type: f1 value: 0.9731597129949509 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmFiNDdjODdjNGJhYjNiZGUwNzc2OTQ0NDhhMjk5ZTFlMjM4NTE5MTViYTBlYzI2ZTE4MzQ5MmE3MTBiZWU0ZiIsInZlcnNpb24iOjF9.amNItmETm5mBYgwTYkYEO7L7mlO6xxPJhHfy8X8LidtLir8euAUxoj4gLro9-NETDGaZOLLvvjx7SRyODMwrAg - type: loss value: 0.1187286302447319 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWFlYThiZGFhYzI4ZjZiNDUyMmQ3ZDVhMGIzZDJhNmU3ZjEwNTU1NTE2YjA3ZjM2NGNlNTA1MmYwNWY4NTdjMiIsInZlcnNpb24iOjF9.qBgBdwqISdVvRHyJQ-8JgqeGGG6J1wrNEcoJiqUgZ8OQIn8FKi6I0xmdBukkoYMapegWqwIGjNVNF4WAsjoyAg --- # entity-extraction This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0808 - Precision: 0.8863 - Recall: 0.9085 - F1: 0.8972 - Accuracy: 0.9775 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2552 | 1.0 | 878 | 0.0808 | 0.8863 | 0.9085 | 0.8972 | 0.9775 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1