File size: 3,148 Bytes
38e25f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
base_model: NousResearch/Llama-2-7b-hf
library_name: peft
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: Ip_test_3000
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. -->
# Ip_test_3000
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6617
- Accuracy: 0.6013
- Precision: 0.5938
- Recall: 0.6210
- F1: 0.6071
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 160
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.96 | 21 | 1.1577 | 0.504 | 0.0 | 0.0 | 0.0 |
| No log | 1.96 | 42 | 0.9227 | 0.504 | 0.0 | 0.0 | 0.0 |
| No log | 2.96 | 63 | 0.6936 | 0.5147 | 0.5108 | 0.5081 | 0.5094 |
| No log | 3.96 | 84 | 0.6954 | 0.496 | 0.4423 | 0.0618 | 0.1085 |
| No log | 4.96 | 105 | 0.6898 | 0.56 | 0.5453 | 0.6801 | 0.6053 |
| No log | 5.96 | 126 | 0.6880 | 0.5653 | 0.5676 | 0.5188 | 0.5421 |
| No log | 6.96 | 147 | 0.6856 | 0.5627 | 0.5780 | 0.4382 | 0.4985 |
| 13.66 | 7.96 | 168 | 0.6873 | 0.5573 | 0.5369 | 0.7823 | 0.6368 |
| 13.66 | 8.96 | 189 | 0.6793 | 0.5893 | 0.5741 | 0.6667 | 0.6169 |
| 13.66 | 9.96 | 210 | 0.6777 | 0.584 | 0.5704 | 0.6532 | 0.6090 |
| 13.66 | 10.96 | 231 | 0.6690 | 0.6133 | 0.5981 | 0.6720 | 0.6329 |
| 13.66 | 11.96 | 252 | 0.6959 | 0.5747 | 0.7087 | 0.2419 | 0.3607 |
| 13.66 | 12.96 | 273 | 0.6691 | 0.6093 | 0.6010 | 0.6317 | 0.6160 |
| 13.66 | 13.96 | 294 | 0.6689 | 0.5987 | 0.5843 | 0.6613 | 0.6204 |
| 10.9484 | 14.96 | 315 | 0.6635 | 0.6 | 0.5984 | 0.5887 | 0.5935 |
| 10.9484 | 15.96 | 336 | 0.6617 | 0.6013 | 0.5938 | 0.6210 | 0.6071 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.3.1.post300
- Datasets 3.2.0
- Tokenizers 0.21.0 |