Training complete

README.md

@@ -1,8 +1,13 @@
 ---
 license: mit
-base_model: xlm-roberta-base
+base_model: Davlan/afro-xlmr-base
 tags:
 - generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
 - name: wolof-finetuned-ner
   results: []
@@ -13,10 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wolof-finetuned-ner
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0688
-- F1 Macro: 0.7853
+- Loss: 0.0719
+- Precision: 0.7785
+- Recall: 0.7891
+- F1: 0.7838
+- Accuracy: 0.9845
 
 ## Model description
 
@@ -45,16 +53,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | F1 Macro |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 226  | 0.1014          | 0.6018   |
-| No log        | 2.0   | 452  | 0.0722          | 0.7584   |
-| 0.164         | 3.0   | 678  | 0.0688          | 0.7853   |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 226  | 0.0899          | 0.6551    | 0.6395 | 0.6472 | 0.9778   |
+| No log        | 2.0   | 452  | 0.0742          | 0.7695    | 0.7721 | 0.7708 | 0.9831   |
+| 0.1592        | 3.0   | 678  | 0.0719          | 0.7785    | 0.7891 | 0.7838 | 0.9845   |
 
 
 ### Framework versions
 
-- Transformers 4.34.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.14.1
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3