metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
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."
model-index:
- name: bert-keyword-discriminator
results: []
bert-keyword-discriminator
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1310
- Precision: 0.8522
- Recall: 0.8868
- Accuracy: 0.9732
- F1: 0.8692
- Ent/precision: 0.8874
- Ent/accuracy: 0.9246
- Ent/f1: 0.9056
- Con/precision: 0.8011
- Con/accuracy: 0.8320
- Con/f1: 0.8163
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 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.1744 | 1.0 | 1875 | 0.1261 | 0.7176 | 0.7710 | 0.9494 | 0.7433 | 0.7586 | 0.8503 | 0.8018 | 0.6514 | 0.6561 | 0.6537 |
0.1261 | 2.0 | 3750 | 0.1041 | 0.7742 | 0.8057 | 0.9600 | 0.7896 | 0.8083 | 0.8816 | 0.8433 | 0.7185 | 0.6957 | 0.7070 |
0.0878 | 3.0 | 5625 | 0.0979 | 0.8176 | 0.8140 | 0.9655 | 0.8158 | 0.8518 | 0.8789 | 0.8651 | 0.7634 | 0.7199 | 0.7410 |
0.0625 | 4.0 | 7500 | 0.0976 | 0.8228 | 0.8643 | 0.9696 | 0.8430 | 0.8515 | 0.9182 | 0.8836 | 0.7784 | 0.7862 | 0.7823 |
0.0456 | 5.0 | 9375 | 0.1047 | 0.8304 | 0.8758 | 0.9704 | 0.8525 | 0.8758 | 0.9189 | 0.8968 | 0.7655 | 0.8133 | 0.7887 |
0.0342 | 6.0 | 11250 | 0.1207 | 0.8363 | 0.8887 | 0.9719 | 0.8617 | 0.8719 | 0.9274 | 0.8988 | 0.7846 | 0.8327 | 0.8080 |
0.0256 | 7.0 | 13125 | 0.1241 | 0.848 | 0.8892 | 0.9731 | 0.8681 | 0.8791 | 0.9299 | 0.9038 | 0.8019 | 0.8302 | 0.8158 |
0.0205 | 8.0 | 15000 | 0.1310 | 0.8522 | 0.8868 | 0.9732 | 0.8692 | 0.8874 | 0.9246 | 0.9056 | 0.8011 | 0.8320 | 0.8163 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1