Training completed!

README.md

@@ -1,32 +1,17 @@
 ---
+library_name: transformers
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
-datasets:
-- emotion
 metrics:
 - accuracy
 - f1
+- precision
+- recall
 model-index:
 - name: distilbert-base-uncased-finetuned-emotion
-  results:
-  - task:
-      name: Text Classification
-      type: text-classification
-    dataset:
-      name: emotion
-      type: emotion
-      config: split
-      split: validation
-      args: split
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.922
-    - name: F1
-      type: f1
-      value: 0.9221508671277507
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,11 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-uncased-finetuned-emotion
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
+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: 0.2195
-- Accuracy: 0.922
-- F1: 0.9222
+- Loss: 0.1401
+- Accuracy: 0.937
+- F1: 0.9371
+- Precision: 0.9375
+- Recall: 0.937
 
 ## Model description
 
@@ -61,21 +48,24 @@ The following hyperparameters were used during training:
 - train_batch_size: 64
 - eval_batch_size: 64
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- 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
-- num_epochs: 2
+- num_epochs: 5
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.8163        | 1.0   | 250  | 0.3067          | 0.907    | 0.9062 |
-| 0.2368        | 2.0   | 500  | 0.2195          | 0.922    | 0.9222 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.204         | 1.0   | 250  | 0.1767          | 0.9285   | 0.9294 | 0.9323    | 0.9285 |
+| 0.1361        | 2.0   | 500  | 0.1595          | 0.93     | 0.9306 | 0.9330    | 0.93   |
+| 0.1057        | 3.0   | 750  | 0.1460          | 0.9375   | 0.9383 | 0.9406    | 0.9375 |
+| 0.0836        | 4.0   | 1000 | 0.1384          | 0.9405   | 0.9405 | 0.9408    | 0.9405 |
+| 0.069         | 5.0   | 1250 | 0.1401          | 0.937    | 0.9371 | 0.9375    | 0.937  |
 
 
 ### Framework versions
 
-- Transformers 4.42.4
-- Pytorch 2.3.1+cu121
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

runs/Jan28_11-02-48_a22d90ff03a6/events.out.tfevents.1738062172.a22d90ff03a6.938.2

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-size 8267
+oid sha256:b351762d474200f5ee62f8e951441bfb1d410c8cd661c3f65ff0e0e980c5a8c2
+size 9093