Update README.md

README.md

@@ -1,70 +1,72 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert/distilroberta-base
-tags:
-- generated_from_trainer
-model-index:
-- name: go-emotions-plus-other-datasets-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-plus-other-datasets-fine-tuned-distilroberta
-
-This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0719
-- Micro Precision: 0.7358
-- Micro Recall: 0.5840
-- Micro F1: 0.6512
-- Macro Precision: 0.5957
-- Macro Recall: 0.4191
-- Macro F1: 0.4670
-- Weighted Precision: 0.7120
-- Weighted Recall: 0.5840
-- Weighted F1: 0.6286
-- Hamming Loss: 0.0266
-
-## 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 |
-|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
-| No log        | 1.0   | 10377 | 0.0788          | 0.7494          | 0.4946       | 0.5959   | 0.5505          | 0.3191       | 0.3567   | 0.7217             | 0.4946          | 0.5559      | 0.0285       |
-| No log        | 2.0   | 20754 | 0.0723          | 0.7354          | 0.5782       | 0.6474   | 0.6452          | 0.3792       | 0.4259   | 0.7312             | 0.5782          | 0.6154      | 0.0268       |
-| No log        | 3.0   | 31131 | 0.0719          | 0.7358          | 0.5840       | 0.6512   | 0.5957          | 0.4191       | 0.4670   | 0.7120             | 0.5840          | 0.6286      | 0.0266       |
-
-
-### 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
+model-index:
+- name: go-emotions-plus-other-datasets-fine-tuned-distilroberta
+  results: []
+datasets:
+- google-research-datasets/go_emotions
+---
+
+<!-- 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-plus-other-datasets-fine-tuned-distilroberta
+
+This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0719
+- Micro Precision: 0.7358
+- Micro Recall: 0.5840
+- Micro F1: 0.6512
+- Macro Precision: 0.5957
+- Macro Recall: 0.4191
+- Macro F1: 0.4670
+- Weighted Precision: 0.7120
+- Weighted Recall: 0.5840
+- Weighted F1: 0.6286
+- Hamming Loss: 0.0266
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
+| No log        | 1.0   | 10377 | 0.0788          | 0.7494          | 0.4946       | 0.5959   | 0.5505          | 0.3191       | 0.3567   | 0.7217             | 0.4946          | 0.5559      | 0.0285       |
+| No log        | 2.0   | 20754 | 0.0723          | 0.7354          | 0.5782       | 0.6474   | 0.6452          | 0.3792       | 0.4259   | 0.7312             | 0.5782          | 0.6154      | 0.0268       |
+| No log        | 3.0   | 31131 | 0.0719          | 0.7358          | 0.5840       | 0.6512   | 0.5957          | 0.4191       | 0.4670   | 0.7120             | 0.5840          | 0.6286      | 0.0266       |
+
+
+### Framework versions
+
+- Transformers 4.47.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.21.0