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--- |
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tags: |
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- generated_from_trainer |
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- twitter-financial-topic-classification |
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- financial |
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- stocks |
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- twitter |
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datasets: |
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- zeroshot/twitter-financial-news-topic |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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widget: |
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- text: >- |
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Here are Thursday's biggest analyst calls: Apple, Amazon, Tesla, Palantir, |
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DocuSign, Exxon & more |
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example_title: Analyst Update' |
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- text: >- |
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LIVE: ECB surprises with 50bps hike, ending its negative rate era. President |
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Christine Lagarde is taking questions |
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example_title: Fed | Central Banks |
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- text: >- |
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Goldman Sachs traders countered the industry’s underwriting slump with |
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revenue gains that raced past analysts’ estimates. The trading operation |
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posted a 32% surge in second-quarter revenue that included another banner |
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period for fixed income |
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example_title: Company | Product News |
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- text: >- |
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China Evergrande Group’s onshore bond holders rejected a plan by the |
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distressed developer to further extend a bond payment which was due on |
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Friday. Rebecca Choong Wilkins reports on Bloomberg Television |
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example_title: Treasuries | Corporate Debt |
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- text: >- |
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Investing Club: Morgan Stanley's dividend, buyback pay us for our patience |
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after quarterly missteps |
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example_title: Dividend |
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- text: >- |
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Investing Club: Our takes on Amazon and Apple heading into next week's |
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earnings reports |
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example_title: Earnings |
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- text: >- |
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JUST RELEASED: Oil Price Dynamics Report → Over the past week, oil prices |
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decreased as supply expectations rose and anticipated demand remained |
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unchanged. |
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example_title: Energy | Oil |
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- text: >- |
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Delta Air Lines fell short of profit expectations in the second quarter and |
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said high operating costs will persist through the rest of the year. |
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Bloomberg Opinion's Brooke Sutherland has more on 'Bloomberg Markets' |
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example_title: Financials |
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- text: >- |
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BREAKING: The Indian rupee plummets to a record 80 per US dollar as foreign |
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investors pull out money from the nation's stocks |
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example_title: Currencies |
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- text: >- |
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Twitter and Elon Musk are now in a high stakes/high risk situation, one |
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analyst said. |
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example_title: General News | Opinion |
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- text: >- |
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Copper prices are signaling that investors are bearish on the economy, |
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strategist says |
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example_title: Gold | Metals | Materials |
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- text: >- |
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Johnson & Johnson CFO Joe Wolk says the company is positioned for the long |
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term and the plans for its consumer operations include an IPO. He speaks on |
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'Bloomberg Markets' |
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example_title: IPO |
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- text: >- |
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Company and Elon Musk are set for a blockbuster courtroom battle over Musk’s |
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attempt to terminate his $44 billion acquisition deal for $TWTR, according |
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to Wedbush analyst Dan Ives. |
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example_title: Legal | Regulation |
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- text: >- |
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Amazon to buy primary health care provider One Medical for roughly $3.9 |
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billion |
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example_title: M&A | Investments |
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- text: >- |
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Barclays Senior Analyst For Equity Research Jason Goldberg: 'Price |
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expectations have changed.'' The global markets business recorded $6.47 |
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billion of revenue in the quarter with rates, commodities and currencies |
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helping drive the fixed-income gains. |
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example_title: Macro |
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- text: >- |
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US stocks push higher in a volatile session. We break it down on The |
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Countdown to The Close |
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example_title: Markets |
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- text: Zelenskyy fires security chiefs over ‘treasonous’ officials |
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example_title: Politics |
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- text: Airbnb co-founder Joe Gebbia is stepping down |
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example_title: Personnel Change |
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- text: French power group EDF requests its shares be suspended |
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example_title: Stock Commentary |
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- text: >- |
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JUST IN: Alibaba shares slide as much as 5.7%, bringing this week's slump to |
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over 15%, after it reportedly faced a data-theft inquiry |
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example_title: Stock Movement |
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model-index: |
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- name: finbert-tone-finetuned-finance-topic-classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: twitter-financial-news-topic |
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type: finance |
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metrics: |
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- type: F1 |
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name: F1 |
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value: 0.910647 |
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- type: accuracy |
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name: accuracy |
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value: 0.910615 |
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pipeline_tag: text-classification |
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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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# finbert-tone-finetuned-finance-topic-classification |
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This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News Topic](https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.509021 |
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- Accuracy: 0.910615 |
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- F1: 0.910647 |
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- Precision: 0.911335 |
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- Recall: 0.910615 |
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## Model description |
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Model determines the financial topic of given tweets over 20 various topics. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance.. |
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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: 64 |
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- eval_batch_size: 64 |
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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: 20 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 266 | 0.5152 | 0.8552 | 0.8504 | 0.8508 | 0.8552 | |
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| 0.7618 | 2.0 | 532 | 0.3999 | 0.8790 | 0.8781 | 0.8842 | 0.8790 | |
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| 0.7618 | 3.0 | 798 | 0.3628 | 0.8943 | 0.8940 | 0.8958 | 0.8943 | |
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| 0.16 | 4.0 | 1064 | 0.3776 | 0.8997 | 0.9001 | 0.9025 | 0.8997 | |
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| 0.16 | 5.0 | 1330 | 0.4286 | 0.8999 | 0.9002 | 0.9022 | 0.8999 | |
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| 0.058 | 6.0 | 1596 | 0.4500 | 0.9043 | 0.9042 | 0.9055 | 0.9043 | |
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| 0.058 | 7.0 | 1862 | 0.4689 | 0.9021 | 0.9017 | 0.9026 | 0.9021 | |
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| 0.0267 | 8.0 | 2128 | 0.4918 | 0.9031 | 0.9029 | 0.9039 | 0.9031 | |
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| 0.0267 | 9.0 | 2394 | 0.5030 | 0.9048 | 0.9049 | 0.9060 | 0.9048 | |
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| 0.0177 | 10.0 | 2660 | 0.5052 | 0.9033 | 0.9034 | 0.9044 | 0.9033 | |
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| 0.0177 | 11.0 | 2926 | 0.5265 | 0.9036 | 0.9034 | 0.9055 | 0.9036 | |
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| 0.013 | 12.0 | 3192 | 0.5267 | 0.9041 | 0.9041 | 0.9058 | 0.9041 | |
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| 0.013 | 13.0 | 3458 | 0.5090 | 0.9106 | 0.9106 | 0.9113 | 0.9106 | |
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| 0.0105 | 14.0 | 3724 | 0.5315 | 0.9067 | 0.9067 | 0.9080 | 0.9067 | |
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| 0.0105 | 15.0 | 3990 | 0.5339 | 0.9084 | 0.9084 | 0.9093 | 0.9084 | |
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| 0.0068 | 16.0 | 4256 | 0.5414 | 0.9072 | 0.9074 | 0.9088 | 0.9072 | |
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| 0.0051 | 17.0 | 4522 | 0.5460 | 0.9092 | 0.9091 | 0.9102 | 0.9092 | |
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| 0.0051 | 18.0 | 4788 | 0.5438 | 0.9072 | 0.9073 | 0.9081 | 0.9072 | |
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| 0.0035 | 19.0 | 5054 | 0.5474 | 0.9072 | 0.9073 | 0.9080 | 0.9072 | |
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| 0.0035 | 20.0 | 5320 | 0.5484 | 0.9079 | 0.9080 | 0.9087 | 0.9079 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
