Update README.md

Adding changes on the read me.

README.md

@@ -2,6 +2,10 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+language: en
+widget:
+- text: "i have been in contact today with the insurance at least 3 times the person i had originally spoken to eileen i believe her name was she was very helpful. the two reps i got after her didnaaaTMt help me solve the issue i was calling about at all. i have been playing middle man between my insurance and doctors office and i have been getting told two different things. all i wanted was for them to get in contact with each other about the issue simple as that. the last person i just spoke to was extremely rude. iaaaTMm very disappointed with the service provided today. iaaaTMm the one paying for the insurance i just wanted to be led in the right direction on what to do."
+- text: "The agent on the phone was very helpful and nice to me."
 metrics:
 - accuracy
 - f1
@@ -15,7 +19,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-uncased-finetuned-surveyclassification
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a custom survey dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.2818
 - Accuracy: 0.9097
@@ -25,13 +29,27 @@ It achieves the following results on the evaluation set:
 
 More information needed
 
-## Intended uses & limitations
+#### Limitations and bias
 
-More information needed
+This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for Text Classification.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
+model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
+text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
+example = "The agent on the phone was very helpful and nice to me."
+results = text_classifier(example)
+print(results)
+```
 
 ## Training and evaluation data
 
-More information needed
+Custom survey dataset.
 
 ## Training procedure