Update README.md

README.md

@@ -7,65 +7,43 @@ sdk: docker
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-
-# Random Baseline Model for Climate Disinformation Classification
-
-## Model Description
-
-This is a random baseline model for the Frugal AI Challenge 2024, specifically for the text classification task of identifying climate disinformation. The model serves as a performance floor, randomly assigning labels to text inputs without any learning.
-
-### Intended Use
-
-- **Primary intended uses**: Baseline comparison for climate disinformation classification models
-- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge
-- **Out-of-scope use cases**: Not intended for production use or real-world classification tasks
-
-## Training Data
-
-The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
-- Size: ~6000 examples
-- Split: 80% train, 20% test
-- 8 categories of climate disinformation claims
-
-### Labels
-0. No relevant claim detected
-1. Global warming is not happening
-2. Not caused by humans
-3. Not bad or beneficial
-4. Solutions harmful/unnecessary
-5. Science is unreliable
-6. Proponents are biased
-7. Fossil fuels are needed
-
-## Performance
-
-### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
-- **Environmental Impact**:
-  - Emissions tracked in gCO2eq
-  - Energy consumption tracked in Wh
-
-### Model Architecture
-The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.
-
-## Environmental Impact
-
-Environmental impact is tracked using CodeCarbon, measuring:
-- Carbon emissions during inference
-- Energy consumption during inference
-
-This tracking helps establish a baseline for the environmental impact of model deployment and inference.
-
-## Limitations
-- Makes completely random predictions
-- No learning or pattern recognition
-- No consideration of input text
-- Serves only as a baseline reference
-- Not suitable for any real-world applications
-
-## Ethical Considerations
-
-- Dataset contains sensitive topics related to climate disinformation
-- Model makes random predictions and should not be used for actual classification
-- Environmental impact is tracked to promote awareness of AI's carbon footprint
-```
+This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:
+
+Loss: 0.3650
+Accuracy: 0.9365
+Model description
+More information needed
+
+Intended uses & limitations
+More information needed
+
+Training and evaluation data
+More information needed
+
+Training procedure
+Training hyperparameters
+The following hyperparameters were used during training:
+
+learning_rate: 1e-05
+train_batch_size: 2
+eval_batch_size: 2
+seed: 42
+gradient_accumulation_steps: 4
+total_train_batch_size: 8
+optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+lr_scheduler_type: linear
+lr_scheduler_warmup_ratio: 0.05
+num_epochs: 7
+Training results
+Training Loss	Epoch	Step	Validation Loss	Accuracy
+1.5669	0.9998	3527	0.2941	0.9211
+1.3782	1.9998	7054	0.4237	0.9216
+1.3008	2.9998	10581	0.4684	0.9113
+1.2606	3.9998	14108	0.4323	0.9204
+1.0533	4.9998	17635	0.3708	0.9331
+0.8921	5.9998	21162	0.3650	0.9365
+Framework versions
+Transformers 4.47.1
+Pytorch 2.5.1+cu121
+Datasets 3.2.0
+Tokenizers 0.21.0