First commit

README.md

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+# Instructions
+
+We've provided a VERY basic Python script to perform inference against the Hugging Face `bert-base-uncased` model. This model takes in a string with a `[MASK]` key. The model will respond with 
+a list of JSON objects containing a probability score, a token, the token string (the value that will fill in `[MASK]`), and the original string with the `[MASK]` key replaced.
+
+For example:
+
+Input:
+```
+"Hello I'm a [MASK] model."
+```
+
+Output:
+```
+[
+    {
+        "score": 0.10731063783168793,
+        "token": 4827,
+        "token_str": "fashion",
+        "sequence": "hello i'm a fashion model."
+    },
+    {
+        "score": 0.08774515986442566,
+        "token": 2535,
+        "token_str": "role",
+        "sequence": "hello i'm a role model."
+    },
+    {
+        "score": 0.05338398739695549,
+        "token": 2047,
+        "token_str": "new",
+        "sequence": "hello i'm a new model."
+    },
+    {
+        "score": 0.04667206481099129,
+        "token": 3565,
+        "token_str": "super",
+        "sequence": "hello i'm a super model."
+    },
+    {
+        "score": 0.027095932513475418,
+        "token": 2986,
+        "token_str": "fine",
+        "sequence": "hello i'm a fine model."
+    }
+]
+```
+
+We would like you to create a REST API service with 2 endpoints:
+* `/inference`: This endpoint should accept the string to replace in a JSON object by POST, and respond with the JSON provided by the model.
+* `/healthcheck`: This endpoint should return an HTTP 200.
+
+It's up to you which REST framework you prefer to use, be it Flask, FastAPI, or Django.
+
+# To perform this excercise:
+1. Fork this repo.
+2. Commit your changes to your fork.
+3. Submit a link to your fork, along with an explanation of your choices (such as REST framework, or anything else you want to point out). This can be in a README in your repo, or a few paragraphs in your response email.
+
+# What we would like to see:
+* A working REST API.
+* Any additional Python requirements added to `requirements.txt`.
+* API documentation! At the very least a README file with the expected input as a cURL call, and sample JSON output.
+    * Bonus points if you add API documentation using something like OpenAPI.
+* Tell us what else you would do to make this model ready for production use.
+
+# Bonus Points:
+* Dockerize the application.
+* Create a pipeline to automatically build and deploy the application to a server.
+* Implement a different model.
+
+# How will you be evaluated
+
+We're not looking for perfection; we really want to see how you tackle a problem, so document your process.
+
+If you had to Google 90% of it to get it to work, THAT'S OK! It's not always about knowing the right answer,
+it's about knowing how to get the right answer. If you found solutions on the Internet, please cite them in your explanation.

bert-inference.py

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+import json
+from transformers import pipeline
+
+unmasker = pipeline('fill-mask', model='bert-base-uncased')
+
+output = unmasker("Hello I'm a [MASK] model.")
+
+print(json.dumps(output, indent=4))

requirements.txt

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+transformers
+torch