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---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: hate-phi
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. -->
# hate-phi
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3268
- Classification Report: precision recall f1-score support
0 0.57 0.08 0.14 438
1 0.91 0.97 0.93 5755
2 0.80 0.79 0.80 1242
accuracy 0.89 7435
macro avg 0.76 0.61 0.62 7435
weighted avg 0.87 0.89 0.87 7435
## 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Classification Report |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.8106 | 0.37 | 25 | 0.4551 | precision recall f1-score support
0 0.18 0.03 0.04 438
1 0.85 0.97 0.91 5755
2 0.75 0.46 0.57 1242
accuracy 0.83 7435
macro avg 0.59 0.49 0.51 7435
weighted avg 0.79 0.83 0.80 7435
|
| 0.3677 | 0.74 | 50 | 0.3374 | precision recall f1-score support
0 0.51 0.09 0.16 438
1 0.91 0.95 0.93 5755
2 0.77 0.83 0.80 1242
accuracy 0.88 7435
macro avg 0.73 0.63 0.63 7435
weighted avg 0.87 0.88 0.87 7435
|
### Framework versions
- PEFT 0.11.1
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |