---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lex-cross-encoder-mdeberta-v3-base-5neg
  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. -->

# lex-cross-encoder-mdeberta-v3-base-5neg

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6811
- Precision: 0.2
- Recall: 1.0
- F2: 0.5556

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F2     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.7455        | 1.0   | 1    | 0.6810          | 0.2       | 1.0    | 0.5556 |
| 0.7455        | 2.0   | 2    | 0.6811          | 0.2       | 1.0    | 0.5556 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.15.2