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Saugatkafley
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mbert-Nepali-NER

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google-bert/bert-base-multilingual-cased
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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: mbert-Nepali-NER
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+ results: []
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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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+ # mbert-Nepali-NER
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+
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+ This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2462
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+ - Precision: 0.3727
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+ - Recall: 0.3154
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+ - F1: 0.3417
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+ - Accuracy: 0.9555
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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: 5e-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: 5
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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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+ | 0.3162 | 0.29 | 500 | 0.2577 | 0.2647 | 0.0692 | 0.1098 | 0.9303 |
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+ | 0.205 | 0.58 | 1000 | 0.2505 | 0.5312 | 0.1308 | 0.2099 | 0.9378 |
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+ | 0.176 | 0.87 | 1500 | 0.2241 | 0.3372 | 0.2231 | 0.2685 | 0.9434 |
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+ | 0.1364 | 1.16 | 2000 | 0.2304 | 0.3125 | 0.1923 | 0.2381 | 0.9473 |
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+ | 0.1188 | 1.44 | 2500 | 0.2136 | 0.25 | 0.3385 | 0.2876 | 0.9413 |
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+ | 0.1056 | 1.73 | 3000 | 0.2134 | 0.3462 | 0.2769 | 0.3077 | 0.9517 |
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+ | 0.1002 | 2.02 | 3500 | 0.2207 | 0.2632 | 0.3077 | 0.2837 | 0.9528 |
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+ | 0.0695 | 2.31 | 4000 | 0.2153 | 0.3041 | 0.3462 | 0.3237 | 0.9524 |
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+ | 0.0701 | 2.6 | 4500 | 0.2038 | 0.2674 | 0.3538 | 0.3046 | 0.9545 |
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+ | 0.0649 | 2.89 | 5000 | 0.2090 | 0.2848 | 0.3462 | 0.3125 | 0.9536 |
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+ | 0.0533 | 3.18 | 5500 | 0.2341 | 0.3913 | 0.2769 | 0.3243 | 0.9546 |
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+ | 0.0422 | 3.47 | 6000 | 0.2459 | 0.4545 | 0.3077 | 0.3670 | 0.9537 |
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+ | 0.0396 | 3.76 | 6500 | 0.2389 | 0.3846 | 0.3077 | 0.3419 | 0.9540 |
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+ | 0.0376 | 4.04 | 7000 | 0.2296 | 0.4091 | 0.2769 | 0.3303 | 0.9550 |
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+ | 0.0221 | 4.33 | 7500 | 0.2533 | 0.3962 | 0.3231 | 0.3559 | 0.9555 |
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+ | 0.021 | 4.62 | 8000 | 0.2609 | 0.3922 | 0.3077 | 0.3448 | 0.9548 |
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+ | 0.0251 | 4.91 | 8500 | 0.2462 | 0.3727 | 0.3154 | 0.3417 | 0.9555 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.1
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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