---
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: startup-score
  results: []
language:
- en
widget:
- text: "Company Name : Dropbox Inc. ,Gender: FEMALE, Company Description: Dropbox lets you save and access all your files and photos in one place for easy sharing. Easily share files & access team content from your computer, mobile or any web browser, Company Website: https://www.dropbox.com/, Job Titles: Chief Operating Officer (COO)/ Head of Operations, Business Model: nan, Revenue: $50,001 - $250,000 (USD), Profit: Not generating profit yet ,Total External Funding: 4000000, Notable Investors: Y Combinator, Sequioa Capital, Competition Region: North America, Team Size: Complementary team with some founders having significant work experience Market Opportunity , Problem to be solved: Product solves a problem and has an attractive niche in a large market. Very strong value proposition to customers. Clear customer identification with unique positioning in mostly untapped market (more than or equals to USD 1 billion)., Innovation: Some unique IP, patents or data (pending patent) ,Business Model: Good revenue model / business model is defined and has been validated with large number of customers ,Scalability: Solution has no issues to scale globally or within home country but scaling has not started ,Traction: Prototype testing with initial customers (Beta testing)"
---

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# startup-score

This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on [Startup Score Dataset](https://huggingface.co/datasets/k011/startup_eligibility_scores).
It achieves the following results on the evaluation set:
- Loss: 0.7827
- Accuracy: 0.25
- F1: 0.3000
- Precision: 0.375
- Recall: 0.25
- Accuracy Label Eligible: 0.0
- Accuracy Label Not eligible: 0.3333

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1