README.md

metadata

license: apache-2.0
tags:
  - chemistry
  - precite
  - chemberta
datasets:
  - blainetrain/precite-dataset-FLP-Test-v11
base_model: seyonec/ChemBERTa-zinc-base-v1
model-index:
  - name: FLP-Test-v11
    results:
      - task:
          type: molecular-property-prediction
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0
          - name: F1
            type: f1
            value: 0
          - name: Precision
            type: precision
            value: 0
          - name: Recall
            type: recall
            value: 0

FLP Test v11

A chemistry prediction model fine-tuned on Precite platform.

Model Details

• Base Model: seyonec/ChemBERTa-zinc-base-v1
• Fine-tuned On: 8 training samples, 2 validation samples (80/20 split)
• Task: Molecular property prediction (4 classes)
• Epochs: 2
• Training Date: 2026-02-04

Performance Metrics (20% Holdout Test Set)

Metric	Value
Accuracy	0.0000
F1 Score	0.0000
Precision	0.0000
Recall	0.0000
Training Loss	1.3330

Label Classes

• high
• low
• medium
• very_low

Usage

This model can be queried through the Precite platform for FLP chemistry predictions.

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("blainetrain/FLP-Test-v11")
tokenizer = AutoTokenizer.from_pretrained("blainetrain/FLP-Test-v11")

Training Data

See the associated dataset: blainetrain/precite-dataset-FLP-Test-v11