diff --git a/README.md b/README.md
index 04b8c2f5..d72d6bfa 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,15 @@
 # Text Embeddings Inference on Habana Gaudi
+## Table of contents
+- [Get started](#get-started)
+- [Supported Models](#supported-models)
+  - [Text Embeddings](#text-embeddings)
+  - [Sequence Classification and Re-Ranking](#sequence-classification-and-re-ranking)
+- [How to Use](#how-to-use)
+  - [Using Re-rankers models](#using-re-rankers-models)
+  - [Using Sequence Classification models](#using-sequence-classification-models)
+  - [Using SPLADE pooling](#using-splade-pooling)
 
+## Get started
 To use [🤗 text-embeddings-inference](https://github.com/huggingface/text-embeddings-inference) on Habana Gaudi/Gaudi2, follow these steps:
 
 1. Pull the official Docker image with:
@@ -32,23 +42,94 @@ To use [🤗 text-embeddings-inference](https://github.com/huggingface/text-embe
         -H 'Content-Type: application/json'
    ```
 
-For more information and documentation about Text Embeddings Inference, checkout [the README](https://github.com/huggingface/text-embeddings-inference#text-embeddings-inference) of the original repo.
+For more information and documentation about Text Embeddings Inference, checkout [README](https://github.com/huggingface/text-embeddings-inference#text-embeddings-inference) of the original repo.
 
-Not all features of TEI are currently supported as this is still a work in progress.
 
-## Validated Models
+## Supported Models
+### Text Embeddings
+`tei-gaudi` currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT model with Alibi positions and Mistral, Alibaba GTE and Qwen2 models with Rope positions.
 
-| Architecture | Model Type | Pooling | Models |
-|--------------|------------|---------|--------|
-| BERT | Embedding | Cls | <li>[BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)</li><li>[sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)</li><li>[sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)</li><li>[sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1)</li><li>[sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)</li><li>[sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)</li> |
-| BERT | Embedding | Splade | <li>[naver/efficient-splade-VI-BT-large-query](https://huggingface.co/naver/efficient-splade-VI-BT-large-query)</li> |
-| MPNet | Embedding | Mean | <li>[sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)</li><li>[sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)</li><li>[sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1)</li> |
-| ALBERT | Embedding | Mean | <li>[sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2)</li> |
-| Mistral | Embedding | Last token | <li>[intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)</li><li>[Salesforce/SFR-Embedding-2_R](https://huggingface.co/Salesforce/SFR-Embedding-2_R)</li> |
-| GTE | Embedding | Cls | <li>[Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5)</li> |
-| JinaBERT | Embedding | Mean | <li>[jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)</li> |
-| Roberta | Sequence Classification | N/A | <li>[SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions)</li> |
+Below are some examples of our validated models:
 
+| Architecture | Pooling | Models |
+|--------------|---------|--------|
+| BERT | Cls/Mean/Last token | <li>[BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)</li><li>[sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)</li><li>[sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)</li><li>[sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1)</li><li>[sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)</li><li>[sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)</li> |
+| BERT | Splade | <li>[naver/efficient-splade-VI-BT-large-query](https://huggingface.co/naver/efficient-splade-VI-BT-large-query)</li> |
+| MPNet | Cls/Mean/Last token | <li>[sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)</li><li>[sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)</li><li>[sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1)</li> |
+| ALBERT | Cls/Mean/Last token | <li>[sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2)</li> |
+| Mistral | Cls/Mean/Last token | <li>[intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)</li><li>[Salesforce/SFR-Embedding-2_R](https://huggingface.co/Salesforce/SFR-Embedding-2_R)</li> |
+| GTE | Cls/Mean/Last token | <li>[Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5)</li> |
+| JinaBERT | Cls/Mean/Last token | <li>[jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)</li> |
+
+### Sequence Classification and Re-Ranking
+`tei-gaudi` currently supports CamemBERT, and XLM-RoBERTa Sequence Classification models with absolute positions.
+
+Below are some examples of the currently supported models:
+| Task               | Model Type  | Model ID                                                                                    |
+|--------------------|-------------|---------------------------------------------------------------------------------------------|
+| Re-Ranking         | XLM-RoBERTa | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)                   |
+| Re-Ranking         | XLM-RoBERTa | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)                     |
+| Sentiment Analysis | RoBERTa     | [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) |
+
+## How to Use
+### Using Re-rankers models
+
+```shell
+model=BAAI/bge-reranker-large
+volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
+
+docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e MAX_WARMUP_SEQUENCE_LENGTH=512 --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tei-gaudi:latest --model-id $model
+```
+
+And then you can rank the similarity between a query and a list of texts with:
+
+```bash
+curl 127.0.0.1:8080/rerank \
+    -X POST \
+    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
+    -H 'Content-Type: application/json'
+```
+
+### Using Sequence Classification models
+
+You can also use classic Sequence Classification models like `SamLowe/roberta-base-go_emotions`:
+
+```shell
+model=SamLowe/roberta-base-go_emotions
+volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
+
+docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e MAX_WARMUP_SEQUENCE_LENGTH=512 --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tei-gaudi:latest --model-id $model
+```
+
+Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:
+
+```bash
+curl 127.0.0.1:8080/predict \
+    -X POST \
+    -d '{"inputs":"I like you."}' \
+    -H 'Content-Type: application/json'
+```
+
+### Using SPLADE pooling
+
+You can choose to activate SPLADE pooling for Bert and Distilbert MaskedLM architectures:
+
+```shell
+docker build -f Dockerfile-hpu -t tei_gaudi .
+model=naver/efficient-splade-VI-BT-large-query
+volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
+
+docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e MAX_WARMUP_SEQUENCE_LENGTH=512 --cap-add=sys_nice --ipc=host tei_gaudi --model-id $model --pooling splade
+```
+
+Once you have deployed the model you can use the `/embed_sparse` endpoint to get the sparse embedding:
+
+```bash
+curl 127.0.0.1:8080/embed_sparse \
+    -X POST \
+    -d '{"inputs":"I like you."}' \
+    -H 'Content-Type: application/json'
+```
 > The license to use TEI on Habana Gaudi is the one of TEI: https://github.com/huggingface/text-embeddings-inference/blob/main/LICENSE
 >
 > Please reach out to api-enterprise@huggingface.co if you have any question.