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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_7labels |
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results: [] |
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datasets: |
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- FinGPT/fingpt-sentiment-train |
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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-7labels |
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This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on 61K financial tweets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7361 |
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- Accuracy: 0.8946 |
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- F1: 0.8969 |
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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: 6.284712783126724e-06 |
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- train_batch_size: 8 |
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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: linear |
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- num_epochs: 10 |
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### Training results |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
