End of training

README.md

@@ -5,6 +5,11 @@ license: mit
 base_model: xlm-roberta-large
 tags:
 - generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
 - name: xlm-roberta-large-ner-demo
   results: []
@@ -17,16 +22,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
 It achieves the following results on the evaluation set:
-- eval_loss: 0.0728
-- eval_precision: 0.9070
-- eval_recall: 0.9260
-- eval_f1: 0.9164
-- eval_accuracy: 0.9789
-- eval_runtime: 99.382
-- eval_samples_per_second: 25.568
-- eval_steps_per_second: 0.805
-- epoch: 2.0
-- step: 954
+- Loss: 0.0976
+- Precision: 0.9340
+- Recall: 0.9404
+- F1: 0.9372
+- Accuracy: 0.9816
 
 ## Model description
 
@@ -51,7 +51,18 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 10
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1657        | 1.0   | 477  | 0.0866          | 0.8655    | 0.8978 | 0.8814 | 0.9752   |
+| 0.0716        | 2.0   | 954  | 0.0801          | 0.9135    | 0.9283 | 0.9208 | 0.9796   |
+| 0.0448        | 3.0   | 1431 | 0.0814          | 0.9244    | 0.9374 | 0.9309 | 0.9805   |
+| 0.0283        | 4.0   | 1908 | 0.0870          | 0.9256    | 0.9367 | 0.9311 | 0.9808   |
+| 0.017         | 5.0   | 2385 | 0.0976          | 0.9340    | 0.9404 | 0.9372 | 0.9816   |
+
 
 ### Framework versions