Text Classification
Transformers
Safetensors
English
bert
proposal-analysis
business
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use JonahDelman/ProposalClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonahDelman/ProposalClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JonahDelman/ProposalClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JonahDelman/ProposalClassifier") model = AutoModelForSequenceClassification.from_pretrained("JonahDelman/ProposalClassifier") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| license: mit | |
| library_name: transformers | |
| tags: | |
| - text-classification | |
| - proposal-analysis | |
| - business | |
| - binary-classification | |
| - bert | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| - loss | |
| model-index: | |
| - name: ProposalClassifier | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: Business Proposals | |
| type: custom | |
| metrics: | |
| - type: accuracy | |
| value: 0.92 | |
| - type: f1 | |
| value: 0.92 | |
| - type: precision | |
| value: 0.92 | |
| - type: recall | |
| value: 0.92 | |
| - type: loss | |
| value: 0.32 | |
| base_model: | |
| - google-bert/bert-base-uncased | |
| pipeline_tag: text-classification | |