End of training

README.md

@@ -17,27 +17,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0335
-- Accuracy: 0.9946
+- Loss: 0.0249
+- Accuracy: 0.9940
 
 ## Model description
 
-This model can categorize a given food product title into Plant-based ("PLANT_BASED") or Animal-based("ANIMAL_BASED").
+More information needed
 
 ## Intended uses & limitations
 
-"whey" is an "ANIMAL_BASED" product derived from Cow's milk. Therefore it must be categorized as as an ANIMAL_BASED food product.
-
-Example usage:
-```
-from transformers import pipeline
-
-text = "whey"
-classifier = pipeline("text-classification", model="nish-j/food_type_classification_model")
-classifier(text)
+More information needed
 
->>> [{'label': 'ANIMAL_BASED', 'score': 0.9941352605819702}]
-```
 ## Training and evaluation data
 
 More information needed
@@ -59,8 +49,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 325  | 0.0324          | 0.9938   |
-| 0.0347        | 2.0   | 650  | 0.0335          | 0.9946   |
+| No log        | 1.0   | 336  | 0.0351          | 0.9933   |
+| 0.0711        | 2.0   | 672  | 0.0249          | 0.9940   |
 
 
 ### Framework versions