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--- |
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license: apache-2.0 |
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base_model: Twitter/twhin-bert-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: financial-twhin-bert-large-3labels-pesudo-bb-only |
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results: [] |
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datasets: |
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- kekunh/stock-related-tweets |
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- zeroshot/twitter-financial-news-sentiment |
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language: |
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- en |
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widget: |
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- text: "$KTOS: Kratos Defense and Security awarded a $39 million sole-source contract for Geolocation Global Support Service" |
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example_title: "Example 1" |
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- text: "$Google parent Alphabet Inc. reported revenue and earnings that fell short of analysts' expectations, showing the company's search advertising juggernaut was not immune to a slowdown in the digital ad market. The shares fell more than 6%." |
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example_title: "Example 2" |
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- text: "$LJPC - La Jolla Pharma to reassess development of LJPC-401" |
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example_title: "Example 3" |
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- text: "Watch $MARK over 43c in after-hours for continuation targeting the 50c area initially" |
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example title: "Example 4" |
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- text: "$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year" |
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example title: "Example 5" |
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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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# financial-twhin-bert-large-3labels-pesudo-bb-only |
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This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on finance-related tweets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4379 |
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- Accuracy: 0.8847 |
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- F1: 0.8857 |
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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: 3.812006227593217e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.5859 | 1.0 | 2283 | 1.5711 | 0.2039 | 0.0800 | |
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| 0.1601 | 2.0 | 4566 | 0.4379 | 0.8847 | 0.8857 | |
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| 0.0875 | 3.0 | 6849 | 0.5111 | 0.8854 | 0.8868 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
