End of training

README.md

@@ -1,6 +1,6 @@
 ---
-license: mit
-base_model: microsoft/deberta-base
+license: apache-2.0
+base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7073333333333334
+      value: 0.71195
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # deberta_finetuned_yahoo_answers_topics
 
-This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the yahoo_answers_topics dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yahoo_answers_topics dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9246
-- Accuracy: 0.7073
+- Loss: 0.9096
+- Accuracy: 0.7119
 
 ## Model description
 
@@ -64,17 +64,17 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 1.197         | 0.03  | 5000  | 1.1306          | 0.6511   |
-| 1.0564        | 0.06  | 10000 | 1.0731          | 0.6690   |
-| 0.9436        | 0.09  | 15000 | 1.0345          | 0.6864   |
-| 1.0601        | 0.11  | 20000 | 0.9684          | 0.6925   |
-| 0.9577        | 0.14  | 25000 | 0.9466          | 0.7015   |
-| 0.9172        | 0.17  | 30000 | 0.9246          | 0.7073   |
+| 1.1025        | 0.03  | 5000  | 1.0702          | 0.6717   |
+| 1.0132        | 0.06  | 10000 | 0.9976          | 0.6834   |
+| 0.8688        | 0.09  | 15000 | 0.9770          | 0.6961   |
+| 0.9964        | 0.11  | 20000 | 0.9356          | 0.7020   |
+| 0.9338        | 0.14  | 25000 | 0.9259          | 0.7090   |
+| 0.9059        | 0.17  | 30000 | 0.9096          | 0.7119   |
 
 
 ### Framework versions
 
 - Transformers 4.34.1
-- Pytorch 2.0.0
-- Datasets 2.14.5
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
 - Tokenizers 0.14.1