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xlm-roberta-base-wnut2017-en

Token Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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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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+ - wnut_17
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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-wnut2017-en
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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: wnut_17
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+ type: wnut_17
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+ config: wnut_17
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+ split: validation
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+ args: wnut_17
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.7219662058371735
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+ - name: Recall
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+ type: recall
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+ value: 0.562200956937799
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+ - name: F1
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+ type: f1
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+ value: 0.6321452589105581
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9589398080467807
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+ ---
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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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+
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+ # xlm-roberta-base-wnut2017-en
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wnut_17 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2319
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+ - Precision: 0.7220
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+ - Recall: 0.5622
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+ - F1: 0.6321
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+ - Accuracy: 0.9589
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 32
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+ - eval_batch_size: 32
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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: 15
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+
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+ ### Training results
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+
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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 | 107 | 0.2789 | 0.4679 | 0.3397 | 0.3936 | 0.9408 |
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+ | No log | 2.0 | 214 | 0.2092 | 0.6875 | 0.5 | 0.5789 | 0.9518 |
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+ | No log | 3.0 | 321 | 0.1968 | 0.6194 | 0.5431 | 0.5787 | 0.9567 |
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+ | No log | 4.0 | 428 | 0.2172 | 0.7212 | 0.5383 | 0.6164 | 0.9586 |
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+ | 0.1523 | 5.0 | 535 | 0.2319 | 0.7220 | 0.5622 | 0.6321 | 0.9589 |
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+ | 0.1523 | 6.0 | 642 | 0.2023 | 0.6180 | 0.5514 | 0.5828 | 0.9577 |
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+ | 0.1523 | 7.0 | 749 | 0.2494 | 0.6895 | 0.5419 | 0.6068 | 0.9589 |
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+ | 0.1523 | 8.0 | 856 | 0.2844 | 0.7018 | 0.5263 | 0.6015 | 0.9578 |
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+ | 0.1523 | 9.0 | 963 | 0.2568 | 0.6808 | 0.5562 | 0.6122 | 0.9592 |
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+ | 0.0294 | 10.0 | 1070 | 0.2453 | 0.6718 | 0.5754 | 0.6198 | 0.9596 |
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+ | 0.0294 | 11.0 | 1177 | 0.2538 | 0.6933 | 0.5706 | 0.6260 | 0.9600 |
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+ | 0.0294 | 12.0 | 1284 | 0.2638 | 0.6865 | 0.5658 | 0.6203 | 0.9593 |
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+ | 0.0294 | 13.0 | 1391 | 0.2744 | 0.6764 | 0.5526 | 0.6083 | 0.9587 |
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+ | 0.0294 | 14.0 | 1498 | 0.2714 | 0.6812 | 0.5622 | 0.6160 | 0.9590 |
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+ | 0.0135 | 15.0 | 1605 | 0.2724 | 0.6830 | 0.5670 | 0.6196 | 0.9593 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2