Update README.md

Adding new information found in evalutation.

README.md

@@ -20,18 +20,44 @@ It achieves the following results on the evaluation set:
 - Loss: 0.7241
 
 ## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
+model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
+
+# We can now use the model in the pipeline.
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Get some text to fool around with for a basic test.
+text = "I loved Oppenheimer and Barbie "
+
+classifier(text) # Let's see if the model works on our example text.
+```
+```
+[{'label': 'JOY', 'score': 0.9845513701438904}]
+```
+## Training Evalutation Results
+
+```python
+{'eval_loss': 0.7240552306175232,
+ 'eval_runtime': 3.7803,
+ 'eval_samples_per_second': 375.896,
+ 'eval_steps_per_second': 23.543,
+ 'epoch': 5.0}
+```
+
+## Overall Model Evaluation Results
+
+```python
+{'accuracy': {'confidence_interval': (0.783, 0.832),
+  'standard_error': 0.01241992329458207,
+  'score': 0.808},
+ 'total_time_in_seconds': 150.93268656500004,
+ 'samples_per_second': 6.625470087086432,
+ 'latency_in_seconds': 0.15093268656500003}
+```
 
 ### Training hyperparameters