---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: bart_developer_keywords
  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. -->

# bart_developer_keywords

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8795

## Model description

This model extracts tech terms, tools, company names from texts so they can easily be aggregated. It is trained to extract tech terms, tools, languages, platforms but may be used on other texts.

## Intended uses & limitations

Use to extract keywords from texts.

Example text: "If a task raises an exception, or a worker process dies, Celery will by default lose the job. So if you happen to reboot or redeploy, any running jobs with be lost to the sands of time."

Output: "Celery, Exception Handling, Worker Process"

Example text: "Spin 2.0 – open-source tool for building and running WebAssembly applications -"

Output: "Spin 2.0, WebAssembly, Open Source"

## Training and evaluation data

More information needed

## Training procedure


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5095        | 0.44  | 50   | 1.1766          |
| 1.1875        | 0.89  | 100  | 0.9652          |
| 1.0428        | 1.33  | 150  | 1.0587          |
| 0.9392        | 1.78  | 200  | 0.8968          |
| 0.786         | 2.22  | 250  | 1.0131          |
| 0.8503        | 2.67  | 300  | 0.8795          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0