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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
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