renjithks
/

distilbert-expense-ner

@@ -17,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 # distilbert-expense-ner
-This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-de-no-da-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-de-no-da-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1641
-- Precision: 0.9489
-- Recall: 0.9430
-- F1: 0.9459
-- Accuracy: 0.9721
 ## Model description
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 112  | 0.1750          | 0.8720    | 0.8891 | 0.8805 | 0.9446   |
-| No log        | 2.0   | 224  | 0.1339          | 0.8804    | 0.9078 | 0.8939 | 0.9515   |
-| No log        | 3.0   | 336  | 0.1157          | 0.9315    | 0.9295 | 0.9305 | 0.9666   |
-| No log        | 4.0   | 448  | 0.1291          | 0.9269    | 0.9326 | 0.9298 | 0.9666   |
-| 0.2164        | 5.0   | 560  | 0.1400          | 0.9247    | 0.9285 | 0.9266 | 0.9666   |
-| 0.2164        | 6.0   | 672  | 0.1463          | 0.9376    | 0.9347 | 0.9362 | 0.9689   |
-| 0.2164        | 7.0   | 784  | 0.1463          | 0.9327    | 0.9337 | 0.9332 | 0.9694   |
-| 0.2164        | 8.0   | 896  | 0.1711          | 0.9376    | 0.9337 | 0.9356 | 0.9661   |
-| 0.0274        | 9.0   | 1008 | 0.1621          | 0.9421    | 0.9440 | 0.9431 | 0.9735   |
-| 0.0274        | 10.0  | 1120 | 0.1641          | 0.9489    | 0.9430 | 0.9459 | 0.9721   |
 ### Framework versions

 # distilbert-expense-ner
+This model is a fine-tuned version of [renjithks/distilbert-cord-ner](https://huggingface.co/renjithks/distilbert-cord-ner) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.2930
+- Precision: 0.5096
+- Recall: 0.4852
+- F1: 0.4971
+- Accuracy: 0.9275
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 22   | 0.3635          | 0.2888    | 0.0945 | 0.1424 | 0.8866   |
+| No log        | 2.0   | 44   | 0.2795          | 0.3213    | 0.3018 | 0.3113 | 0.8982   |
+| No log        | 3.0   | 66   | 0.2432          | 0.4243    | 0.4034 | 0.4136 | 0.9161   |
+| No log        | 4.0   | 88   | 0.2446          | 0.4615    | 0.4654 | 0.4635 | 0.9193   |
+| No log        | 5.0   | 110  | 0.2410          | 0.5143    | 0.4810 | 0.4971 | 0.9293   |
+| No log        | 6.0   | 132  | 0.2598          | 0.5283    | 0.4612 | 0.4925 | 0.9305   |
+| No log        | 7.0   | 154  | 0.2963          | 0.5230    | 0.4485 | 0.4829 | 0.9268   |
+| No log        | 8.0   | 176  | 0.2753          | 0.4928    | 0.4838 | 0.4883 | 0.9283   |
+| No log        | 9.0   | 198  | 0.2897          | 0.5194    | 0.4725 | 0.4948 | 0.9295   |
+| No log        | 10.0  | 220  | 0.2930          | 0.5096    | 0.4852 | 0.4971 | 0.9275   |
 ### Framework versions