---
base_model: mistralai/Mistral-7B-v0.1
tags:
- alignment-handbook
- trl
- orpo
- generated_from_trainer
- trl
- orpo
- generated_from_trainer
- entity linking
datasets:
- arynkiewicz/anydef-kilt-tasks-v2
model-index:
- name: anydef-orpo-v2
  results: []
license: apache-2.0
inference: false
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# anydef-orpo-v2

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the arynkiewicz/anydef-kilt-tasks-v2 dataset.

Find out about Model description, Intended uses & limitations and Training and evaluation data on our [github](https://github.com/daisd-ai/universal-el).

This is an updated version of the anydef model. The primary goal was to use an improved dataset during fine-tuning, enabling the model to better understand nuances.
Overall, anydef-v2 offers better performance in benchmarks, and manual inspection of the results suggests that the model has indeed improved.

Precision (%):
| Dataset   |  anydef  | anydef-v2   |
|------------|------------|------------|
| RSS-500 | 66.23| 66.89|
| ISTEX-1000| 86.72|  85.82|
| Reuters-128| 63.8| 64.88|
| TweekiGold| 75.23| 75.93|

Retrieval rate (%):
| Dataset   |  anydef  | anydef-v2   |
|------------|------------|------------|
| RSS-500 | 82.78| 84.11|
| ISTEX-1000| 97.91|   97.76|
| Reuters-128| 80.47| 83.33|
| TweekiGold| 89.93| 91.67|

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: inverse_sqrt
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.43.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1