Upload README.md with huggingface_hub

README.md

@@ -1,13 +1,13 @@
 ---
 language: en
 license: mit
-model_id: test_trainer
+model_id: Statement_Equivalence
 developers: Matt Stammers
 model_type: BERT-Base-Uncased
 model_summary: This model Compares the similarity of two text objects.
 shared_by: Matt Stammers
 finetuned_from: Glue
-repo: https://huggingface.co/MattStammers/test-trainer?text=I+like+you.+I+love+you
+repo: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
 paper: N/A
 demo: N/A
 direct_use: Test it out here
@@ -18,10 +18,10 @@ bias_risks_limitations: Biases inherent in Glue also apply here
 bias_recommendations: Do not be surprised if unusual results are obtained
 get_started_code: "\n    ``` python \n    # Use a pipeline as a high-level helper\n\
   \        from transformers import pipeline\n\n        pipe = pipeline(\"text-classification\"\
-  , model=\"MattStammers/test-trainer\")\n    # Load model directly\n        from\
-  \ transformers import AutoTokenizer, AutoModelForSequenceClassification\n\n    \
-  \    tokenizer = AutoTokenizer.from_pretrained(\"MattStammers/test-trainer\")\n\
-  \        model = AutoModelForSequenceClassification.from_pretrained(\"MattStammers/test-trainer\"\
+  , model=\"MattStammers/Statement_Equivalence\")\n    # Load model directly\n   \
+  \     from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\
+  \n        tokenizer = AutoTokenizer.from_pretrained(\"MattStammers/Statement_Equivalence\"\
+  )\n        model = AutoModelForSequenceClassification.from_pretrained(\"MattStammers/Statement_Equivalence\"\
   )\n    ```\n                        "
 training_data: 'See Glue Dataset: https://huggingface.co/datasets/glue'
 preprocessing: Sentence Pairs to analyse similarity
@@ -48,7 +48,7 @@ more_information: Can be made available on request
 model_card_authors: Matt Stammers
 model_card_contact: Matt Stammers
 model-index:
-- name: test_trainer
+- name: statement
   results:
   - task:
       type: text-classification
@@ -62,7 +62,7 @@ model-index:
       value: 0.8945578231292517
 ---
 
-# Model Card for test_trainer
+# Model Card for Statement_Equivalence
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -87,7 +87,7 @@ This model Compares the similarity of two text objects.
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** https://huggingface.co/MattStammers/test-trainer?text=I+like+you.+I+love+you
+- **Repository:** https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you
 - **Paper [optional]:** N/A
 - **Demo [optional]:** N/A
 
@@ -134,12 +134,12 @@ Use the code below to get started with the model.
     # Use a pipeline as a high-level helper
         from transformers import pipeline
 
-        pipe = pipeline("text-classification", model="MattStammers/test-trainer")
+        pipe = pipeline("text-classification", model="MattStammers/Statement_Equivalence")
     # Load model directly
         from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-        tokenizer = AutoTokenizer.from_pretrained("MattStammers/test-trainer")
-        model = AutoModelForSequenceClassification.from_pretrained("MattStammers/test-trainer")
+        tokenizer = AutoTokenizer.from_pretrained("MattStammers/Statement_Equivalence")
+        model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Statement_Equivalence")
     ```