---
license: apache-2.0
language:
- en
library_name: transformers
---
# ModernBERT Environment Claims Classifier

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) trained on the [QuotaClimat FrugalAIChallenge dataset](https://huggingface.co/datasets/QuotaClimat/frugalaichallenge-text-train).

## Training Details

The model was trained using the following configuration:

```python
training_args = TrainingArguments(
   output_dir="ModernBERT-envclaims-v0",
   per_device_train_batch_size=32,
   per_device_eval_batch_size=16,
   learning_rate=2e-5,
   num_train_epochs=3,
   bf16=True,
   optim="adamw_torch_fused",
   
   # Logging & Evaluation
   logging_strategy="steps", 
   logging_steps=100,
   eval_strategy="epoch",
   save_strategy="epoch", 
   save_total_limit=2,
   load_best_model_at_end=True,
   metric_for_best_model="f1",
   
   # Training optimization
   weight_decay=0.01,
   lr_scheduler_type="cosine",
   warmup_ratio=0.1,
   
   # Hub parameters
   push_to_hub=True,
   hub_strategy="every_save"
)
```

## Model Performance

The model achieved an F1 score of 0.76 on the evaluation set.

## Usage
You can use this model directly with the Hugging Face Transformers library:
```python
from transformers import pipeline
classifier = pipeline(
    "text-classification",
    modelcamillebrl/ModernBERT-envclaims-v1"
)
text = "Your claim here"
class_predicted = classifier(text)
```

The model classifies texts into the following categories:
* Label 0: not_relevant 
* Label 1: not_happening
* Label 2: not_human
* Label 3: not_bad
* Label 4: solutions_harmful_unnecessary
* Label 5: science_unreliable
* Label 6: proponents_biased
* Label 7: fossil_fuels_needed