---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- modernbert
- zeroshot
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: modernbert-zeroshot-xnli-eng-0.1
  results: []
datasets:
- facebook/xnli
language:
- en
pipeline_tag: zero-shot-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# modernbert-zeroshot-xnli-eng-0.1

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on 10% of the english subset of facebook/xnli dataset.
It achieves the following results on the evaluation set:
- Test Loss: 0.3539
- F1: 0.8596


## Model description

answerdotai/ModernBERT-base

## Intended uses & limitations

## Training and evaluation data

10% of the english subset of facebook/xnli dataset.

## Training procedure

trained on a single gpu for apx. 20 mins.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0801        | 0.1629 | 200  | 0.8987          | 0.5868   | 0.5820 | 0.6637    | 0.5868 |
| 0.6737        | 0.3257 | 400  | 0.4906          | 0.8184   | 0.8181 | 0.8340    | 0.8184 |
| 0.5361        | 0.4886 | 600  | 0.3931          | 0.8723   | 0.8724 | 0.8759    | 0.8723 |
| 0.4933        | 0.6515 | 800  | 0.3664          | 0.8782   | 0.8786 | 0.8853    | 0.8782 |
| 0.4728        | 0.8143 | 1000 | 0.4300          | 0.8303   | 0.8306 | 0.8604    | 0.8303 |
| 0.4434        | 0.9772 | 1200 | 0.3210          | 0.8922   | 0.8923 | 0.8925    | 0.8922 |
| 0.2859        | 1.1401 | 1400 | 0.3657          | 0.8483   | 0.8502 | 0.8651    | 0.8483 |
| 0.2768        | 1.3029 | 1600 | 0.4162          | 0.8403   | 0.8397 | 0.8520    | 0.8403 |
| 0.258         | 1.4658 | 1800 | 0.4072          | 0.8543   | 0.8543 | 0.8634    | 0.8543 |
| 0.2657        | 1.6287 | 2000 | 0.3763          | 0.8463   | 0.8460 | 0.8537    | 0.8463 |
| 0.2721        | 1.7915 | 2200 | 0.3940          | 0.8463   | 0.8464 | 0.8595    | 0.8463 |
| 0.2878        | 1.9544 | 2400 | 0.3539          | 0.8603   | 0.8596 | 0.8641    | 0.8603 |
| 0.1366        | 2.1173 | 2600 | 0.7444          | 0.8343   | 0.8371 | 0.8738    | 0.8343 |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0