---
base_model: castorini/afriteva_v2_base
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: cross_amh
  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. -->

# cross_amh

This model is a fine-tuned version of [castorini/afriteva_v2_base](https://huggingface.co/castorini/afriteva_v2_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7276
- Model Preparation Time: 0.0061
- Accuracy: {'accuracy': 0.18509375}

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Model Preparation Time | Accuracy                  |
|:-------------:|:-------:|:-----:|:---------------:|:----------------------:|:-------------------------:|
| 1.4008        | 2.2173  | 5000  | 1.0351          | 0.0061                 | {'accuracy': 0.17328125}  |
| 1.0872        | 4.4346  | 10000 | 0.8747          | 0.0061                 | {'accuracy': 0.17896875}  |
| 0.9522        | 6.6519  | 15000 | 0.8086          | 0.0061                 | {'accuracy': 0.18146875}  |
| 0.8673        | 8.8692  | 20000 | 0.7648          | 0.0061                 | {'accuracy': 0.183390625} |
| 0.7922        | 11.0865 | 25000 | 0.7456          | 0.0061                 | {'accuracy': 0.184375}    |
| 0.7608        | 13.3038 | 30000 | 0.7276          | 0.0061                 | {'accuracy': 0.18509375}  |


### Framework versions

- PEFT 0.7.1
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1