license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: bart_developer_keywords
results: []
bart_developer_keywords
This model is a fine-tuned version of 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