README.md

---
license: apache-2.0
tags:
  - chemistry
  - precite
  - chemberta
datasets:
  - blainetrain/precite-dataset-FLP-Test-v10
base_model: seyonec/ChemBERTa-zinc-base-v1
model-index:
  - name: FLP-Test-v10
    results:
      - task:
          type: molecular-property-prediction
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5000
          - name: F1
            type: f1
            value: 0.3333
          - name: Precision
            type: precision
            value: 0.2500
          - name: Recall
            type: recall
            value: 0.5000
---

# FLP Test v10

A chemistry prediction model fine-tuned on Precite platform.

## Model Details

- **Base Model**: [seyonec/ChemBERTa-zinc-base-v1](https://huggingface.co/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** | 0.5000 |
| **F1 Score** | 0.3333 |
| **Precision** | 0.2500 |
| **Recall** | 0.5000 |
| Training Loss | 1.4379 |

## Label Classes

- `high`
- `low`
- `medium`
- `very_low`

## Usage

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

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("blainetrain/FLP-Test-v10")
tokenizer = AutoTokenizer.from_pretrained("blainetrain/FLP-Test-v10")
```

## Training Data

See the associated dataset: [blainetrain/precite-dataset-FLP-Test-v10](https://huggingface.co/datasets/blainetrain/precite-dataset-FLP-Test-v10)