Update README.md

README.md

@@ -2,15 +2,46 @@
 license: mit
 base_model: roberta-base
 tags:
-- generated_from_trainer
+  - stress
+  - classification
+  - glassdoor
 metrics:
 - accuracy
 - f1
 - precision
 - recall
+widget:
+  - text: >-
+      They also caused so much stress because some leaders valued optics over output.: Stressed
+  - text: >-
+      Way too much work pressure.: Stressed
+  - text: >-
+      Understaffed, lots of deck revisions, unpredictable, terrible technology.: Stressed
+  - text: >-
+      Nice environment, good work life balance.: Not Stressed
 model-index:
-- name: roberta-base_stress_classification
-  results: []
+- name: roberta-base_topic_classification_nyt_news
+  results:
+    - task:
+          name: Text Classification
+          type: text-classification
+      dataset:
+          name: New_York_Times_Topics
+          type: News
+      metrics:
+          - type: F1
+            name: F1
+            value: 0.97
+          - type: accuracy
+            name: accuracy
+            value: 0.97
+          - type: precision
+            name: precision
+            value: 0.97
+          - type: recall
+            name: recall
+            value: 0.97
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,7 +49,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # roberta-base_stress_classification
 
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glassdoor dataset based on 100000 employees' reviews.
 It achieves the following results on the evaluation set:
 - Loss: 0.1800
 - Accuracy: 0.9647
@@ -26,17 +57,14 @@ It achieves the following results on the evaluation set:
 - Precision: 0.9647
 - Recall: 0.9647
 
-## Model description
+## Training data
 
-More information needed
+Training data was classified as follow:
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+class |Description
+-|-
+0 |Not Stressed
+1 |Stressed
 
 ## Training procedure
 
@@ -63,9 +91,36 @@ The following hyperparameters were used during training:
 | 0.0618        | 5.0   | 40000 | 0.2128          | 0.9536   | 0.9536 | 0.9546    | 0.9536 |
 
 
+### Model performance
+
+-|precision|recall|f1|support
+-|-|-|-|-
+Not Stressed|0.96|0.97|0.97|10000
+Stressed|0.97|0.96|0.97|10000
+ | | | |
+accuracy|||0.97|20000
+macro avg|0.97|0.97|0.97|20000
+weighted avg|0.97|0.97|0.97|20000
+
+
+### How to use roberta-base_topic_classification_nyt_news with HuggingFace
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
+model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
+
+text = "They also caused so much stress because some leaders valued optics over output."
+pipe(text)
+
+[{'label': 'Stressed', 'score': 0.9959163069725037}]
+
 ### Framework versions
 
 - Transformers 4.32.1
 - Pytorch 2.1.0+cu121
 - Datasets 2.12.0
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2