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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- favsbot |
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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: xlm-roberta-base-NER-favsbot |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: favsbot |
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type: favsbot |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.5555555555555556 |
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- name: Recall |
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type: recall |
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value: 0.4722222222222222 |
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- name: F1 |
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type: f1 |
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value: 0.5105105105105106 |
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- name: Accuracy |
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type: accuracy |
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value: 0.6900452488687783 |
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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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# xlm-roberta-base-NER-favsbot |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0572 |
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- Precision: 0.5556 |
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- Recall: 0.4722 |
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- F1: 0.5105 |
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- Accuracy: 0.6900 |
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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: 1.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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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| No log | 1.0 | 4 | 2.4303 | 0.1448 | 0.3556 | 0.2058 | 0.1855 | |
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| No log | 2.0 | 8 | 2.3220 | 0.1465 | 0.3556 | 0.2075 | 0.1991 | |
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| No log | 3.0 | 12 | 2.1842 | 0.2486 | 0.2389 | 0.2436 | 0.4593 | |
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| No log | 4.0 | 16 | 1.9552 | 0.4 | 0.0111 | 0.0216 | 0.4367 | |
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| No log | 5.0 | 20 | 1.6989 | 0.0 | 0.0 | 0.0 | 0.4321 | |
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| No log | 6.0 | 24 | 1.6532 | 0.5 | 0.0056 | 0.0110 | 0.4344 | |
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| No log | 7.0 | 28 | 1.5724 | 0.3649 | 0.15 | 0.2126 | 0.5045 | |
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| No log | 8.0 | 32 | 1.5164 | 0.3654 | 0.2111 | 0.2676 | 0.5271 | |
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| No log | 9.0 | 36 | 1.4448 | 0.4203 | 0.1611 | 0.2329 | 0.5090 | |
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| No log | 10.0 | 40 | 1.3922 | 0.4833 | 0.1611 | 0.2417 | 0.5158 | |
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| No log | 11.0 | 44 | 1.3409 | 0.5395 | 0.2278 | 0.3203 | 0.5498 | |
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| No log | 12.0 | 48 | 1.2831 | 0.5824 | 0.2944 | 0.3911 | 0.5950 | |
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| No log | 13.0 | 52 | 1.2269 | 0.5714 | 0.3556 | 0.4384 | 0.6335 | |
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| No log | 14.0 | 56 | 1.1766 | 0.5625 | 0.4 | 0.4675 | 0.6606 | |
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| No log | 15.0 | 60 | 1.1408 | 0.5540 | 0.4278 | 0.4828 | 0.6674 | |
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| No log | 16.0 | 64 | 1.1159 | 0.56 | 0.4667 | 0.5091 | 0.6810 | |
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| No log | 17.0 | 68 | 1.0908 | 0.5658 | 0.4778 | 0.5181 | 0.6855 | |
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| No log | 18.0 | 72 | 1.0722 | 0.5658 | 0.4778 | 0.5181 | 0.6923 | |
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| No log | 19.0 | 76 | 1.0615 | 0.5592 | 0.4722 | 0.5120 | 0.6900 | |
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| No log | 20.0 | 80 | 1.0572 | 0.5556 | 0.4722 | 0.5105 | 0.6900 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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