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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
widget:
- text: 'Broadcom agreed to acquire cloud computing company VMware in a $61 billion
(€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and
almost tripling its software-related revenue to about 45% of its total sales.
By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520
of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion
of VMware''s net debt.'
- text: 'Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that
the country could start supplying Europe with liquefied natural gas (LNG) in as
soon as three years by converting an existing LNG import facility on Canada’s
Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada
cares about is that the new LNG facility uses a low-emission process for the gas
and is capable of transitioning to exporting hydrogen later on.'
- text: 'Google is being investigated by the UK’s antitrust watchdog for its dominance
in the "ad tech stack," the set of services that facilitate the sale of online
advertising space between advertisers and sellers. Google has strong positions
at various levels of the ad tech stack and charges fees to both publishers and
advertisers. A step back: UK Competition and Markets Authority has also been investigating
whether Google and Meta colluded over ads, probing into the advertising agreement
between the two companies, codenamed Jedi Blue.'
- text: 'Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon
Musk pledged to put up an additional $6.25 billion of his own wealth to fund the
$44 billion takeover deal, lifting the total to $33.5 billion from an initial
$27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he''s
stepping down, but would stay on Twitter’s board \“until his term expires at the
2022 meeting of stockholders."'
base_model: bert-base-cased
model-index:
- name: bert-keyword-extractor
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. -->
# bert-keyword-extractor
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1341
- Precision: 0.8565
- Recall: 0.8874
- Accuracy: 0.9738
- F1: 0.8717
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1688 | 1.0 | 1875 | 0.1233 | 0.7194 | 0.7738 | 0.9501 | 0.7456 |
| 0.1219 | 2.0 | 3750 | 0.1014 | 0.7724 | 0.8166 | 0.9606 | 0.7939 |
| 0.0834 | 3.0 | 5625 | 0.0977 | 0.8280 | 0.8263 | 0.9672 | 0.8272 |
| 0.0597 | 4.0 | 7500 | 0.0984 | 0.8304 | 0.8680 | 0.9704 | 0.8488 |
| 0.0419 | 5.0 | 9375 | 0.1042 | 0.8417 | 0.8687 | 0.9717 | 0.8550 |
| 0.0315 | 6.0 | 11250 | 0.1161 | 0.8520 | 0.8839 | 0.9729 | 0.8677 |
| 0.0229 | 7.0 | 13125 | 0.1282 | 0.8469 | 0.8939 | 0.9734 | 0.8698 |
| 0.0182 | 8.0 | 15000 | 0.1341 | 0.8565 | 0.8874 | 0.9738 | 0.8717 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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