Text Classification
Transformers
Safetensors
English
modernbert
upwork
employer-detection
entity-extraction
gating-model
Eval Results (legacy)
text-embeddings-inference
Instructions to use mwilly2003/upwork-employer-detection-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mwilly2003/upwork-employer-detection-modernbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mwilly2003/upwork-employer-detection-modernbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mwilly2003/upwork-employer-detection-modernbert-base") model = AutoModelForSequenceClassification.from_pretrained("mwilly2003/upwork-employer-detection-modernbert-base") - Notebooks
- Google Colab
- Kaggle
Upwork Employer Detection (ModernBERT Gate)
A binary text classifier that identifies freelancer job postings authored by an employer with an identifiable company name. It is built as the first stage of an extraction pipeline: this model filters the input stream, a larger model extracts company name and domain on positives.
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Model tree for mwilly2003/upwork-employer-detection-modernbert-base
Base model
answerdotai/ModernBERT-baseEvaluation results
- Precisionself-reported0.742
- Recallself-reported0.941
- F1self-reported0.830