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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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- f1 |
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widget: |
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- text: 'Broadcom agreed to acquire cloud computing company VMware in a $61 billion |
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(€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and |
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almost tripling its software-related revenue to about 45% of its total sales. |
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By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 |
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of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion |
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of VMware''s net debt.' |
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- text: 'Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that |
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the country could start supplying Europe with liquefied natural gas (LNG) in as |
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soon as three years by converting an existing LNG import facility on Canada’s |
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Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada |
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cares about is that the new LNG facility uses a low-emission process for the gas |
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and is capable of transitioning to exporting hydrogen later on.' |
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- text: 'Google is being investigated by the UK’s antitrust watchdog for its dominance |
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in the "ad tech stack," the set of services that facilitate the sale of online |
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advertising space between advertisers and sellers. Google has strong positions |
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at various levels of the ad tech stack and charges fees to both publishers and |
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advertisers. A step back: UK Competition and Markets Authority has also been investigating |
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whether Google and Meta colluded over ads, probing into the advertising agreement |
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between the two companies, codenamed Jedi Blue.' |
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- text: 'Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon |
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Musk pledged to put up an additional $6.25 billion of his own wealth to fund the |
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$44 billion takeover deal, lifting the total to $33.5 billion from an initial |
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$27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he''s |
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stepping down, but would stay on Twitter’s board \“until his term expires at the |
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2022 meeting of stockholders."' |
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base_model: bert-base-cased |
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model-index: |
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- name: bert-keyword-discriminator |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-keyword-discriminator |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1310 |
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- Precision: 0.8522 |
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- Recall: 0.8868 |
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- Accuracy: 0.9732 |
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- F1: 0.8692 |
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- Ent/precision: 0.8874 |
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- Ent/accuracy: 0.9246 |
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- Ent/f1: 0.9056 |
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- Con/precision: 0.8011 |
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- Con/accuracy: 0.8320 |
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- Con/f1: 0.8163 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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