update

README.md

@@ -1,3 +1,22 @@
 ---
-license: mit
+tags:
+- text-classification
+library_name: generic
 ---
+
+# Text Classification repository template
+
+This is a template repository for Text Classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
+
+1. Specify the requirements by defining a `requirements.txt` file.
+2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
+
+## How to start
+First create a repo in https://hf.co/new. 
+Then clone this template and push it to your repo.
+```
+git clone https://huggingface.co/templates/text-classification
+cd text-classification
+git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
+git push --force
+```

pipeline.py

@@ -0,0 +1,30 @@
+from typing import  Dict, List
+import numpy as np
+import tensorflow as tf
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+        # raise NotImplementedError(
+        #     "Please implement PreTrainedPipeline __init__ function"
+        # )
+        pass
+
+    def __call__(self, inputs: str) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
+        """
+        # IMPLEMENT_THIS
+        # raise NotImplementedError(
+        #     "Please implement PreTrainedPipeline __call__ function"
+        # )
+        return [[{"label": inputs, "score":0.2}]]

requirements.txt

@@ -0,0 +1 @@
+tensorflow