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---
library_name: transformers
license: other
base_model: DedalusHealthCare/tinybert-mlm-en
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: tinybert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinybert
This model is a fine-tuned version of [DedalusHealthCare/tinybert-mlm-en](https://huggingface.co/DedalusHealthCare/tinybert-mlm-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5185
- Accuracy: 0.9816
- F1: 0.0
- Precision: 0.0
- Recall: 0.0
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
| No log | 0.2857 | 1 | 0.6663 | 0.7953 | 0.0 | 0.0 | 0.0 |
| No log | 0.5714 | 2 | 0.6612 | 0.8189 | 0.0 | 0.0 | 0.0 |
| No log | 0.8571 | 3 | 0.6516 | 0.8766 | 0.0 | 0.0 | 0.0 |
| No log | 1.1429 | 4 | 0.6373 | 0.9081 | 0.0 | 0.0 | 0.0 |
| No log | 1.4286 | 5 | 0.6185 | 0.9423 | 0.0 | 0.0 | 0.0 |
| No log | 1.7143 | 6 | 0.5955 | 0.9685 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 7 | 0.5687 | 0.9790 | 0.0 | 0.0 | 0.0 |
| No log | 2.2857 | 8 | 0.5431 | 0.9816 | 0.0 | 0.0 | 0.0 |
| No log | 2.5714 | 9 | 0.5185 | 0.9816 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.6.0+cu124
- Datasets 2.16.0
- Tokenizers 0.20.3
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