Training complete

README.md

@@ -1,9 +1,8 @@
 ---
 license: mit
+base_model: camembert-base
 tags:
 - generated_from_trainer
-datasets:
-- wikiann
 metrics:
 - precision
 - recall
@@ -11,29 +10,7 @@ metrics:
 - accuracy
 model-index:
 - name: camembert-finetuned-ner
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: wikiann
-      type: wikiann
-      config: fr
-      split: validation
-      args: fr
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.8826469710534169
-    - name: Recall
-      type: recall
-      value: 0.8992854971115841
-    - name: F1
-      type: f1
-      value: 0.8908885542168675
-    - name: Accuracy
-      type: accuracy
-      value: 0.9472222222222222
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # camembert-finetuned-ner
 
-This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the wikiann dataset.
+This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2199
-- Precision: 0.8826
-- Recall: 0.8993
-- F1: 0.8909
-- Accuracy: 0.9472
+- Loss: 0.2215
+- Precision: 0.8871
+- Recall: 0.9019
+- F1: 0.8944
+- Accuracy: 0.9476
 
 ## Model description
 
@@ -78,14 +55,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2955        | 1.0   | 2500 | 0.2667          | 0.8603    | 0.8784 | 0.8693 | 0.9369   |
-| 0.2089        | 2.0   | 5000 | 0.2269          | 0.8680    | 0.8953 | 0.8814 | 0.9443   |
-| 0.1617        | 3.0   | 7500 | 0.2199          | 0.8826    | 0.8993 | 0.8909 | 0.9472   |
+| 0.2902        | 1.0   | 2500 | 0.2712          | 0.8608    | 0.8755 | 0.8681 | 0.9364   |
+| 0.2127        | 2.0   | 5000 | 0.2221          | 0.8858    | 0.8934 | 0.8896 | 0.9437   |
+| 0.1575        | 3.0   | 7500 | 0.2215          | 0.8871    | 0.9019 | 0.8944 | 0.9476   |
 
 
 ### Framework versions
 
-- Transformers 4.30.1
-- Pytorch 2.0.1+cu118
-- Datasets 2.12.0
-- Tokenizers 0.13.3
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1