add tokenizer readme for usage (#1981)

extensions/tokenizers/README.md

@@ -24,3 +24,83 @@ You can pull the module from the central Maven repository by including the follo
     <version>0.18.0</version>
 </dependency>
 ```
+
+## Usage
+
+### Use DJL HuggingFace model converter (experimental)
+
+If you are trying to convert a complete HuggingFace (transformers) model,
+you can try to use our all-in-one conversion solution to convert to Java:
+
+```bash
+python3 -m pip install -r src/main/python/requirements.txt
+python3 src/main/python/model_zoo_importer.py -m deepset/bert-base-cased-squad2
+```
+
+This will generate a zip file into your local folder:
+
+```
+model/nlp/question_answer/ai/djl/huggingface/pytorch/deepset/bert-base-cased-squad2/0.0.1/bert-base-cased-squad2.zip
+```
+
+Then, all you need to do, is to load this model in DJL:
+
+```java
+Criteria<QAInput, String> criteria = Criteria.builder()
+    .setTypes(QAInput.class, String.class)
+    .optModelPath(Paths.get("model/nlp/question_answer/ai/djl/huggingface/pytorch/deepset/bert-base-cased-squad2/0.0.1/bert-base-cased-squad2.zip"))
+    .optProgress(new ProgressBar()).build();
+```
+
+Currently, this converter support:
+
+- fill-mask
+- question-answering
+- sentence-similarity
+- text-classification
+- token-classification
+
+### From HuggingFace AutoTokenizer
+In most of the cases, you can easily use a pre-existing tokenizer in DJL:
+
+Python
+
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-dot-v5")
+```
+
+Java
+
+```java
+HuggingFaceTokenizer tokenizer = HuggingFaceTokenizer.newInstance("sentence-transformers/msmarco-distilbert-dot-v5")
+```
+
+This way requires network connection to huggingface repo.
+The way to determine if you can use this way is through looking into the "Files and versions" in [HuggingFace model tab](https://huggingface.co/sentence-transformers/msmarco-distilbert-dot-v5)
+and see if there is a `tokenizer.json`. 
+
+If there is a `tokenizer.json`, you can get it directly through DJL. Otherwise, use the other way below to obtain a `tokenizer.json`.
+
+### From HuggingFace Pipeline
+
+If you are trying to get tokenizer from a HuggingFace pipeline,
+you can use the followings to extract `tokenizer.json` file.
+
+Python
+
+```python
+ pipeline.tokenizer.save_pretrained("./")
+```
+
+From your local directory, you will find a `tokenizer.json` file.
+
+Java
+
+```java
+HuggingFaceTokenizer tokenizer = HuggingFaceTokenizer.newInstance(Paths.get("./tokenizer.json"))
+```
+
+### From pretrained json file
+
+Same as above step, just save your tokenizer into `tokenizer.json` (done by huggingface).