File size: 1,909 Bytes
2973aeb 6f796b0 2973aeb 6f796b0 2973aeb 6f796b0 |
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 |
---
license: apache-2.0
base_model: albert/albert-base-v1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: toxigen-albert-binary-clsf
results: []
datasets:
- toxigen/toxigen-data
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/octoopt/huggingface/runs/39ugf5uc)
# toxigen-albert-binary-clsf
This model is a fine-tuned version of [albert/albert-base-v1](https://huggingface.co/albert/albert-base-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0000
- Recall: 1.0000
- Accuracy: 1.0000
## Model description
More information needed
## Intended uses & limitations
Finetuning `albert/albert-base-v1` on the `toxigen/toxigen-data` dataset on the task of binary classfication.
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|
| 0.0 | 1.0 | 3137 | 0.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0001 | 2.0 | 6274 | 0.0000 | 1.0000 | 1.0000 | 1.0000 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1 |