---
license: apache-2.0
base_model: jinaai/jina-embeddings-v2-base-code
tags:
- generated_from_trainer
model-index:
- name: jina_embeddings_v2_base_code_multi_regression-simple
  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. -->

[<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/amazingvince/huggingface/runs/213lyzv9)
# jina_embeddings_v2_base_code_multi_regression-simple

This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) on the amazingvince/the-stack-smol-xs-scored-and-annotated-all dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6117
- Mse: 0.6117

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 90085
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Mse    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5055        | 0.3871 | 100  | 0.6117          | 0.6117 |
| 0.5288        | 0.7743 | 200  | 0.6113          | 0.6113 |
| 0.5981        | 1.1614 | 300  | 0.6117          | 0.6117 |
| 0.6077        | 1.5485 | 400  | 0.6125          | 0.6125 |
| 0.6943        | 1.9356 | 500  | 0.6117          | 0.6117 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1