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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-finetuned-ner-word-embedding-model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-finetuned-ner-word-embedding-model |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the the combined training dataset(tweetner7(train_2021)+augmented dataset(train_2021) |
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using word embedding technique). |
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Training Dataset: lsoni/combined_tweetner7_word_embedding_augmented_dataset |
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Evaluation Dataset: lsoni/combined_tweetner7_word_embedding_augmented_dataset_eval |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5411 |
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- Precision: 0.6710 |
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- Recall: 0.5062 |
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- F1: 0.5771 |
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- Accuracy: 0.8650 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.7157 | 1.0 | 624 | 0.5842 | 0.6958 | 0.4498 | 0.5464 | 0.8608 | |
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| 0.5299 | 2.0 | 1248 | 0.5449 | 0.6662 | 0.4897 | 0.5645 | 0.8635 | |
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| 0.4648 | 3.0 | 1872 | 0.5411 | 0.6710 | 0.5062 | 0.5771 | 0.8650 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.10.1 |
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- Tokenizers 0.12.1 |
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