annahaz
/

xlm-roberta-base-finetuned-misogyny-sexism

@@ -19,12 +19,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0211
-- Accuracy: 0.9949
-- F1: 0.9948
-- Precision: 0.9906
-- Recall: 0.9989
-- Mae: 0.0051
 ## Model description
@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
-| 0.3841        | 1.0   | 2778  | 0.4443          | 0.8027   | 0.7919 | 0.8030    | 0.7811 | 0.1973 |
-| 0.334         | 2.0   | 5556  | 0.4946          | 0.8058   | 0.8272 | 0.7225    | 0.9674 | 0.1942 |
-| 0.2995        | 3.0   | 8334  | 0.2693          | 0.8912   | 0.8951 | 0.8344    | 0.9653 | 0.1088 |
-| 0.2675        | 4.0   | 11112 | 0.2575          | 0.9145   | 0.9168 | 0.8612    | 0.98   | 0.0855 |
-| 0.2263        | 5.0   | 13890 | 0.1100          | 0.9611   | 0.9598 | 0.9514    | 0.9684 | 0.0389 |
-| 0.2089        | 6.0   | 16668 | 0.0999          | 0.9712   | 0.9706 | 0.9524    | 0.9895 | 0.0288 |
-| 0.1871        | 7.0   | 19446 | 0.0644          | 0.9782   | 0.9774 | 0.9769    | 0.9779 | 0.0218 |
-| 0.1795        | 8.0   | 22224 | 0.0264          | 0.9924   | 0.9922 | 0.9865    | 0.9979 | 0.0076 |
-| 0.144         | 9.0   | 25002 | 0.0231          | 0.9924   | 0.9922 | 0.9855    | 0.9989 | 0.0076 |
-| 0.1296        | 10.0  | 27780 | 0.0211          | 0.9949   | 0.9948 | 0.9906    | 0.9989 | 0.0051 |
 ### Framework versions
 - Transformers 4.20.1
-- Pytorch 1.9.0+cu111
 - Datasets 2.3.2
 - Tokenizers 0.12.1

 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.9064
+- Accuracy: 0.8334
+- F1: 0.3322
+- Precision: 0.2498
+- Recall: 0.4961
+- Mae: 0.1666
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
+| 0.3869        | 1.0   | 2395  | 0.2905          | 0.8778   | 0.3528 | 0.3164    | 0.3988 | 0.1222 |
+| 0.3539        | 2.0   | 4790  | 0.4143          | 0.8278   | 0.3465 | 0.2536    | 0.5467 | 0.1722 |
+| 0.3124        | 3.0   | 7185  | 0.3327          | 0.8568   | 0.3583 | 0.2864    | 0.4786 | 0.1432 |
+| 0.2817        | 4.0   | 9580  | 0.5621          | 0.7329   | 0.3092 | 0.1972    | 0.7160 | 0.2671 |
+| 0.2651        | 5.0   | 11975 | 0.4376          | 0.8520   | 0.3607 | 0.2821    | 0.5    | 0.1480 |
+| 0.2249        | 6.0   | 14370 | 0.5581          | 0.8326   | 0.3312 | 0.2485    | 0.4961 | 0.1674 |
+| 0.1958        | 7.0   | 16765 | 0.6728          | 0.8382   | 0.3234 | 0.2484    | 0.4630 | 0.1618 |
+| 0.1899        | 8.0   | 19160 | 0.7404          | 0.8304   | 0.3316 | 0.2471    | 0.5039 | 0.1696 |
+| 0.1619        | 9.0   | 21555 | 0.8309          | 0.8461   | 0.3382 | 0.2639    | 0.4708 | 0.1539 |
+| 0.1453        | 10.0  | 23950 | 0.9064          | 0.8334   | 0.3322 | 0.2498    | 0.4961 | 0.1666 |
 ### Framework versions
 - Transformers 4.20.1
+- Pytorch 1.12.0+cu102
 - Datasets 2.3.2
 - Tokenizers 0.12.1