Update README.md

README.md

@@ -1,70 +1,79 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert/distilroberta-base
-tags:
-- generated_from_trainer
-model-index:
-- name: go-emotions-fine-tuned-distilroberta
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# go-emotions-fine-tuned-distilroberta
-
-This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0841
-- Micro Precision: 0.6789
-- Micro Recall: 0.5047
-- Micro F1: 0.5790
-- Macro Precision: 0.5559
-- Macro Recall: 0.4000
-- Macro F1: 0.4502
-- Weighted Precision: 0.6538
-- Weighted Recall: 0.5047
-- Weighted F1: 0.5577
-- Hamming Loss: 0.0308
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3.0
-
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Weighted Precision | Weighted Recall | Weighted F1 | Hamming Loss |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
-| 0.1062        | 1.0   | 5427  | 0.0889          | 0.6956          | 0.4498       | 0.5464   | 0.5087          | 0.3111       | 0.3537   | 0.6246             | 0.4498          | 0.4936      | 0.0314       |
-| 0.0828        | 2.0   | 10854 | 0.0834          | 0.7042          | 0.4798       | 0.5707   | 0.5874          | 0.3562       | 0.4108   | 0.6872             | 0.4798          | 0.5306      | 0.0303       |
-| 0.0704        | 3.0   | 16281 | 0.0841          | 0.6789          | 0.5047       | 0.5790   | 0.5559          | 0.4000       | 0.4502   | 0.6538             | 0.5047          | 0.5577      | 0.0308       |
-
-
-### Framework versions
-
-- Transformers 4.47.0
-- Pytorch 2.3.1+cu121
-- Datasets 2.20.0
-- Tokenizers 0.21.0
+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert/distilroberta-base
+tags:
+- generated_from_trainer
+- sentiment_analysis
+model-index:
+- name: go-emotions-fine-tuned-distilroberta
+  results: []
+datasets:
+- google-research-datasets/go_emotions
+language:
+- en
+metrics:
+- recall
+- precision
+- f1
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# go-emotions-fine-tuned-distilroberta
+
+This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0841
+- Micro Precision: 0.6789
+- Micro Recall: 0.5047
+- Micro F1: 0.5790
+- Macro Precision: 0.5559
+- Macro Recall: 0.4000
+- Macro F1: 0.4502
+- Weighted Precision: 0.6538
+- Weighted Recall: 0.5047
+- Weighted F1: 0.5577
+- Hamming Loss: 0.0308
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Weighted Precision | Weighted Recall | Weighted F1 | Hamming Loss |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
+| 0.1062        | 1.0   | 5427  | 0.0889          | 0.6956          | 0.4498       | 0.5464   | 0.5087          | 0.3111       | 0.3537   | 0.6246             | 0.4498          | 0.4936      | 0.0314       |
+| 0.0828        | 2.0   | 10854 | 0.0834          | 0.7042          | 0.4798       | 0.5707   | 0.5874          | 0.3562       | 0.4108   | 0.6872             | 0.4798          | 0.5306      | 0.0303       |
+| 0.0704        | 3.0   | 16281 | 0.0841          | 0.6789          | 0.5047       | 0.5790   | 0.5559          | 0.4000       | 0.4502   | 0.6538             | 0.5047          | 0.5577      | 0.0308       |
+
+
+### Framework versions
+
+- Transformers 4.47.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.21.0