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---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-distilled-squad
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased-distilled-squad_07112024T113312
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. -->
# distilbert-base-uncased-distilled-squad_07112024T113312
This model is a fine-tuned version of [distilbert/distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8186
- F1: 0.7804
- Learning Rate: 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Rate |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log | 1.0 | 121 | 1.1959 | 0.5325 | 0.0000 |
| No log | 2.0 | 242 | 1.0212 | 0.6350 | 0.0000 |
| No log | 3.0 | 363 | 0.9424 | 0.6796 | 0.0000 |
| No log | 4.0 | 484 | 0.8570 | 0.7203 | 0.0000 |
| 1.0311 | 5.0 | 605 | 0.8567 | 0.7359 | 0.0000 |
| 1.0311 | 6.0 | 726 | 0.8271 | 0.7530 | 0.0000 |
| 1.0311 | 7.0 | 847 | 0.8387 | 0.7629 | 0.0000 |
| 1.0311 | 8.0 | 968 | 0.8491 | 0.7690 | 0.0000 |
| 0.4626 | 9.0 | 1089 | 0.7954 | 0.7809 | 2e-06 |
| 0.4626 | 10.0 | 1210 | 0.8186 | 0.7804 | 0.0 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.19.1
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