diff --git a/src/trustyai/language/detoxify/README.md b/src/trustyai/language/detoxify/README.md
new file mode 100644
index 0000000..beed8c9
--- /dev/null
+++ b/src/trustyai/language/detoxify/README.md
@@ -0,0 +1,45 @@
+# TrustyAI-Detoxify
+Algorithms and tools for detecting and fixing hate speech, abuse and profanity in content generated by Large Language Models (_LLMs_).
+
+## T-MaRCo
+
+T-MaRCo is an extension of the work [Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
+](https://arxiv.org/abs/2212.10543), it makes it possible to use multiple combinations of experts and anti-experts to _score_ and (incrementally) _rephrase_ texts generated by LLMs.
+
+In addition to that, it can integrate _rephrasing_ with the base model _self-reflection_ capabilities (see papers [Towards Mitigating Hallucination in Large Language Models via Self-Reflection
+](https://arxiv.org/abs/2310.06271) and [N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
+](https://arxiv.org/abs/2310.18679)).
+
+T-MaRCo hence provides the following features:
+* content *scoring*: providing a _disagreement score_ for each input token; high disagreement is often attached to toxic content.
+* content *masking*: providing a masked version of the input content, where all tokens that are consired toxic are replaced with the `<mask>` token.  
+* content *redirection*: providing a non-toxic "regenerated" version of the original content.
+
+How to use T-MaRCo:
+```python
+from trustyai.detoxify import TMaRCo
+
+# instantiate T-MaRCo
+tmarco = TMaRCo(expert_weights=[-1, 2])
+
+# load pretrained anti-expert and expert models
+tmarco.load_models(["trustyai/gminus", "trustyai/gplus"])
+
+# pick up some text generated by a LLM
+text = "Stand by me, just as long as you fucking stand by me"
+
+# generate T-MaRCo disagreement scores
+scores = tmarco.score([text]) # '[0.78664607 0.06622718 0.02403926 5.331921 0.49842355 0.46609956 0.22441313 0.43487906 0.51990145 1.9062967  0.64200985 0.30269763 1.7964466 ]' 
+
+# mask tokens having high disagreement scores
+masked_text = tmarco.mask([text], scores=scores) # 'Stand by me<mask> just as long as you<mask> stand by<mask>'
+
+# rephrase masked tokens
+rephrased = tmarco.rephrase([text], [masked_text]) # 'Stand by me and just as long as you want stand by me''
+
+# combine rephrasing and a base model self-reflection capabilities
+reflected = tmarco.reflect([text]) # '["'Stand by me in the way I want stand by you and in the ways I need you to standby me'."]'
+
+```
+
+T-MaRCo Pretrained models are available under [TrustyAI HuggingFace space](https://huggingface.co/trustyai) at https://huggingface.co/trustyai/gminus and https://huggingface.co/trustyai/gplus.
\ No newline at end of file