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---
license: mit
base_model: avsolatorio/GIST-large-Embedding-v0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: my-clf-microsoft
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. -->
# my-clf-microsoft
This model is a fine-tuned version of [avsolatorio/GIST-large-Embedding-v0](https://huggingface.co./avsolatorio/GIST-large-Embedding-v0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2381
- F1: 0.5822
- Roc Auc: 0.7634
- Accuracy: 0.1786
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log | 1.0 | 50 | 0.3147 | 0.0656 | 0.5250 | 0.0 |
| No log | 2.0 | 100 | 0.2808 | 0.2809 | 0.6082 | 0.0536 |
| No log | 3.0 | 150 | 0.2539 | 0.3854 | 0.6521 | 0.0357 |
| No log | 4.0 | 200 | 0.2451 | 0.4085 | 0.6582 | 0.0714 |
| No log | 5.0 | 250 | 0.2351 | 0.4365 | 0.6734 | 0.1071 |
| No log | 6.0 | 300 | 0.2361 | 0.4977 | 0.7133 | 0.125 |
| No log | 7.0 | 350 | 0.2325 | 0.5629 | 0.7433 | 0.1607 |
| No log | 8.0 | 400 | 0.2294 | 0.5488 | 0.7401 | 0.1964 |
| No log | 9.0 | 450 | 0.2336 | 0.5750 | 0.7567 | 0.1964 |
| 0.1718 | 10.0 | 500 | 0.2342 | 0.5695 | 0.7563 | 0.1964 |
| 0.1718 | 11.0 | 550 | 0.2354 | 0.5809 | 0.7648 | 0.1964 |
| 0.1718 | 12.0 | 600 | 0.2349 | 0.5862 | 0.7658 | 0.1786 |
| 0.1718 | 13.0 | 650 | 0.2390 | 0.5811 | 0.7645 | 0.1786 |
| 0.1718 | 14.0 | 700 | 0.2367 | 0.5841 | 0.7633 | 0.2143 |
| 0.1718 | 15.0 | 750 | 0.2376 | 0.5778 | 0.7606 | 0.1786 |
| 0.1718 | 16.0 | 800 | 0.2381 | 0.5822 | 0.7634 | 0.1786 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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