File size: 2,479 Bytes
5319050 a71be41 5319050 a71be41 788c19e a71be41 5319050 a71be41 5319050 a71be41 5319050 a71be41 c05a360 5319050 a71be41 5319050 a71be41 5319050 a71be41 5319050 a71be41 5319050 a71be41 c05a360 a71be41 788c19e | 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 87 88 89 90 | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
- ml-intern
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: spam-email-distilbert
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. -->
# spam-email-distilbert
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
- Accuracy: 0.9874
- F1: 0.9785
- Precision: 0.9705
- Recall: 0.9867
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0399 | 1.0 | 1034 | 0.0875 | 0.9807 | 0.9656 | 0.9965 | 0.9367 |
| 0.0005 | 2.0 | 2068 | 0.0645 | 0.9874 | 0.9785 | 0.9705 | 0.9867 |
### Framework versions
- Transformers 5.8.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
<!-- ml-intern-provenance -->
## Generated by ML Intern
This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'Aime2k2/spam-email-distilbert'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```
For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
|