End of training

README.md

@@ -1,24 +1,23 @@
 ---
-license: apache-2.0
-base_model: bert-base-uncased
+base_model: google/reformer-crime-and-punishment
 tags:
 - generated_from_trainer
 metrics:
 - accuracy
 model-index:
-- name: test_model2
+- name: reformer_model
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# test_model2
+# reformer_model
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [google/reformer-crime-and-punishment](https://huggingface.co/google/reformer-crime-and-punishment) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3250
-- Accuracy: 0.863
+- Loss: 0.6693
+- Accuracy: 0.561
 
 ## Model description
 
@@ -49,13 +48,13 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.5093        | 1.0   | 625  | 0.3441          | 0.852    |
-| 0.3595        | 2.0   | 1250 | 0.3250          | 0.863    |
+| 0.6841        | 1.0   | 625  | 0.6725          | 0.559    |
+| 0.6789        | 2.0   | 1250 | 0.6693          | 0.561    |
 
 
 ### Framework versions
 
-- Transformers 4.35.2
-- Pytorch 2.1.1+cpu
-- Datasets 2.18.0
-- Tokenizers 0.15.0
+- Transformers 4.40.2
+- Pytorch 2.3.0+cpu
+- Datasets 2.19.1
+- Tokenizers 0.19.1