End of training

README.md

@@ -1,72 +1,68 @@
 ---
-license: apache-2.0
-tags:
-  - chemistry
-  - precite
-  - chemberta
-datasets:
-  - blainetrain/precite-dataset-FLP-Test-v11
+library_name: transformers
 base_model: seyonec/ChemBERTa-zinc-base-v1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
 model-index:
-  - name: FLP-Test-v11
-    results:
-      - task:
-          type: molecular-property-prediction
-        metrics:
-          - name: Accuracy
-            type: accuracy
-            value: 0.5000
-          - name: F1
-            type: f1
-            value: 0.5000
-          - name: Precision
-            type: precision
-            value: 0.5000
-          - name: Recall
-            type: recall
-            value: 0.5000
+- name: FLP-Test-v11
+  results: []
 ---
 
-# FLP Test v11
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# FLP-Test-v11
+
+This model is a fine-tuned version of [seyonec/ChemBERTa-zinc-base-v1](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3951
+- Accuracy: 0.0
+- Precision: 0.0
+- Recall: 0.0
+- F1: 0.0
+
+## Model description
 
-A chemistry prediction model fine-tuned on Precite platform.
+More information needed
 
-## Model Details
+## Intended uses & limitations
 
-- **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
+More information needed
 
-## Performance Metrics (20% Holdout Test Set)
+## Training and evaluation data
 
-| Metric | Value |
-|--------|-------|
-| **Accuracy** | 0.5000 |
-| **F1 Score** | 0.5000 |
-| **Precision** | 0.5000 |
-| **Recall** | 0.5000 |
-| Training Loss | 1.3901 |
+More information needed
 
-## Label Classes
+## Training procedure
 
-- `high`
-- `low`
-- `medium`
-- `very_low`
+### Training hyperparameters
 
-## Usage
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 2
 
-This model can be queried through the Precite platform for FLP chemistry predictions.
+### Training results
 
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1  |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---:|
+| No log        | 1.0   | 2    | 1.3951          | 0.0      | 0.0       | 0.0    | 0.0 |
+| No log        | 2.0   | 4    | 1.3983          | 0.0      | 0.0       | 0.0    | 0.0 |
 
-model = AutoModelForSequenceClassification.from_pretrained("blainetrain/FLP-Test-v11")
-tokenizer = AutoTokenizer.from_pretrained("blainetrain/FLP-Test-v11")
-```
 
-## Training Data
+### Framework versions
 
-See the associated dataset: [blainetrain/precite-dataset-FLP-Test-v11](https://huggingface.co/datasets/blainetrain/precite-dataset-FLP-Test-v11)
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.5.0
+- Tokenizers 0.22.2

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:78066d95624cdde21e80bca5026c624681a64b793d4d1859fdca7d86177a31cd
+oid sha256:3ca2d2d62cb6afe5944335ac67892f0cc2b83cde2a6abd50a6fb70461ed06e4a
 size 176440432