gabrielkytz
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Text Classification

Transformers

PyTorch

roberta

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gabrielkytz commited on Oct 15, 2023

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README.md +15 -15
pytorch_model.bin +1 -1

README.md CHANGED Viewed

@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.2370
-- Accuracy: 0.8797
-- F1: 0.6330
-- Recall: 0.6116
-- Precision: 0.6592
 ## Model description
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| No log        | 1.0   | 173  | 0.9661          | 0.8928   | 0.6658 | 0.6312 | 0.7375    |
-| No log        | 2.0   | 346  | 1.0282          | 0.8913   | 0.6589 | 0.6269 | 0.7022    |
-| 0.0216        | 3.0   | 519  | 1.0163          | 0.8797   | 0.6690 | 0.6728 | 0.6684    |
-| 0.0216        | 4.0   | 692  | 1.1821          | 0.8841   | 0.6255 | 0.6097 | 0.6503    |
-| 0.0216        | 5.0   | 865  | 1.1308          | 0.8913   | 0.6450 | 0.5910 | 0.7461    |
-| 0.0112        | 6.0   | 1038 | 1.2350          | 0.8812   | 0.6355 | 0.6122 | 0.6646    |
-| 0.0112        | 7.0   | 1211 | 1.2786          | 0.8884   | 0.6476 | 0.6150 | 0.6920    |
-| 0.0112        | 8.0   | 1384 | 1.2351          | 0.8928   | 0.6464 | 0.6131 | 0.6946    |
-| 0.0014        | 9.0   | 1557 | 1.2341          | 0.8754   | 0.6228 | 0.6099 | 0.6379    |
-| 0.0014        | 10.0  | 1730 | 1.2370          | 0.8797   | 0.6330 | 0.6116 | 0.6592    |
 ### Framework versions

 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.8419
+- Accuracy: 0.8926
+- F1: 0.6997
+- Recall: 0.6785
+- Precision: 0.7256
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| No log        | 1.0   | 247  | 0.3108          | 0.9007   | 0.5703 | 0.5571 | 0.8927    |
+| No log        | 2.0   | 494  | 0.3131          | 0.8926   | 0.6777 | 0.6337 | 0.7649    |
+| 0.3289        | 3.0   | 741  | 0.5054          | 0.8896   | 0.6971 | 0.7116 | 0.6857    |
+| 0.3289        | 4.0   | 988  | 0.6063          | 0.8987   | 0.7195 | 0.7089 | 0.7309    |
+| 0.1268        | 5.0   | 1235 | 0.7100          | 0.8967   | 0.6993 | 0.6836 | 0.7173    |
+| 0.1268        | 6.0   | 1482 | 0.7683          | 0.8977   | 0.7039 | 0.6861 | 0.7257    |
+| 0.0346        | 7.0   | 1729 | 0.8041          | 0.8936   | 0.7012 | 0.6701 | 0.7415    |
+| 0.0346        | 8.0   | 1976 | 0.8185          | 0.8956   | 0.6988 | 0.6815 | 0.7191    |
+| 0.0107        | 9.0   | 2223 | 0.8280          | 0.8926   | 0.6943 | 0.6739 | 0.7185    |
+| 0.0107        | 10.0  | 2470 | 0.8419          | 0.8926   | 0.6997 | 0.6785 | 0.7256    |
 ### Framework versions

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