Create README.md

README.md

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+---
+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