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docs: Fix links to ML examples (#5754)
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Fixed broken links in ml.md and also updated column heading to match
latest README

Co-authored-by: caitlin_wheeless <caitlin@humansignal.com>
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caitlinwheeless and caitlin_wheeless committed Apr 19, 2024
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Expand Up @@ -117,27 +117,30 @@ The [ML backend repo](https://github.com/HumanSignal/label-studio-ml-backend) in

Some of them work without any additional configuration. Check the **Required parameters** column to see if you need to set any additional parameters. If the model has required parameters, you can set those parameters in `docker-compose.yml` within the model directory.

- **Auto-annotation** column indicates if the model can be used for auto-annotation in Label Studio: pre-annotated data when opening the labeling page, run predictions for the batch of data.
- **Interactive mode** column indicates if the model can be used for interactive labeling in Label Studio: see interactive predictions when performing actions on labeling page.
- **Training** column indicates if the model can be used for training in Label Studio: update the model state based the submitted annotations.

| MODEL_NAME | Description | Auto-annotation | Interactive mode | Training | Required parameters |
- **Pre-annotation** column indicates if the model can be used for pre-annotation in Label Studio:
you can see pre-annotated data when opening the labeling page or after running predictions for a batch of data.
- **Interactive mode** column indicates if the model can be used for interactive labeling in Label Studio: see
interactive predictions when performing actions on labeling page.
- **Training** column indicates if the model can be used for training in Label Studio: update the model state based the
submitted annotations.

| MODEL_NAME | Description | Pre-annotation | Interactive mode | Training | Required parameters |
|--------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|-----------------|------------------|----------|-----------------------------------------------|
| [segment_anything_model](/label_studio_ml/examples/segment_anything_model) | Image segmentation by [Meta](https://segment-anything.com/) | ❌ | ✅ | ❌ | None |
| [llm_interactive](/label_studio_ml/examples/llm_interactive) | Prompt engineering with [OpenAI](https://platform.openai.com/), Azure LLMs. | ✅ | ✅ | ✅ | OPENAI_API_KEY |
| [grounding_dino](/label_studio_ml/examples/grounding_dino) | Object detection with prompts. [Details](https://github.com/IDEA-Research/GroundingDINO) | ❌ | ✅ | ❌ | None |
| [tesseract](/label_studio_ml/examples/tesseract) | Interactive OCR. [Details](https://github.com/tesseract-ocr/tesseract) | ❌ | ✅ | ❌ | None |
| [easyocr](/label_studio_ml/examples/easyocr) | Automated OCR. [EasyOCR](https://github.com/JaidedAI/EasyOCR) | ✅ | ❌ | ❌ | None |
| [spacy](/label_studio_ml/examples/spacy) | NER by [SpaCy](https://spacy.io/) | ✅ | ❌ | ❌ | None |
| [flair](/label_studio_ml/examples/flair) | NER by [flair](https://flairnlp.github.io/) | ✅ | ❌ | ❌ | None |
| [bert_classifier](/label_studio_ml/examples/bert_classifier) | Text classification with [Huggingface](https://huggingface.co/transformers/v3.0.2/model_doc/auto.html#automodelforsequenceclassification) | ✅ | ❌ | ✅ | None |
| [huggingface_llm](/label_studio_ml/examples/huggingface_llm) | LLM inference with [Hugging Face](https://huggingface.co/tasks/text-generation) | ✅ | ❌ | ❌ | None |
| [huggingface_ner](/label_studio_ml/examples/huggingface_ner) | NER by [Hugging Face](https://huggingface.co/docs/transformers/en/tasks/token_classification) | ✅ | ❌ | ✅ | None |
| [nemo_asr](/label_studio_ml/examples/nemo_asr) | Speech ASR by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) | ✅ | ❌ | ❌ | None |
| [mmdetection](/label_studio_ml/examples/mmdetection-3) | Object Detection with [OpenMMLab](https://github.com/open-mmlab/mmdetection) | ✅ | ❌ | ❌ | None |
| [sklearn_text_classifier](/label_studio_ml/examples/sklearn_text_classifier) | Text classification with [scikit-learn](https://scikit-learn.org/stable/) | ✅ | ❌ | ✅ | None |
| [interactive_substring_matching](/label_studio_ml/examples/interactive_substring_matching) | Simple keywords search | ❌ | ✅ | ❌ | None |
| [langchain_search_agent](/label_studio_ml/examples/langchain_search_agent) | RAG pipeline with Google Search and [Langchain](https://langchain.com/) | ✅ | ✅ | ✅ | OPENAI_API_KEY, GOOGLE_CSE_ID, GOOGLE_API_KEY |
| [segment_anything_model](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/segment_anything_model) | Image segmentation by [Meta](https://segment-anything.com/) | ❌ | ✅ | ❌ | None |
| [llm_interactive](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/llm_interactive) | Prompt engineering with [OpenAI](https://platform.openai.com/), Azure LLMs. | ✅ | ✅ | ✅ | OPENAI_API_KEY |
| [grounding_dino](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/grounding_dino) | Object detection with prompts. [Details](https://github.com/IDEA-Research/GroundingDINO) | ❌ | ✅ | ❌ | None |
| [tesseract](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/tesseract) | Interactive OCR. [Details](https://github.com/tesseract-ocr/tesseract) | ❌ | ✅ | ❌ | None |
| [easyocr](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/easyocr) | Automated OCR. [EasyOCR](https://github.com/JaidedAI/EasyOCR) | ✅ | ❌ | ❌ | None |
| [spacy](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/spacy) | NER by [SpaCy](https://spacy.io/) | ✅ | ❌ | ❌ | None |
| [flair](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/flair) | NER by [flair](https://flairnlp.github.io/) | ✅ | ❌ | ❌ | None |
| [bert_classifier](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/bert_classifier) | Text classification with [Huggingface](https://huggingface.co/transformers/v3.0.2/model_doc/auto.html#automodelforsequenceclassification) | ✅ | ❌ | ✅ | None |
| [huggingface_llm](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/huggingface_llm) | LLM inference with [Hugging Face](https://huggingface.co/tasks/text-generation) | ✅ | ❌ | ❌ | None |
| [huggingface_ner](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/huggingface_ner) | NER by [Hugging Face](https://huggingface.co/docs/transformers/en/tasks/token_classification) | ✅ | ❌ | ✅ | None |
| [nemo_asr](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/nemo_asr) | Speech ASR by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) | ✅ | ❌ | ❌ | None |
| [mmdetection](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/mmdetection-3) | Object Detection with [OpenMMLab](https://github.com/open-mmlab/mmdetection) | ✅ | ❌ | ❌ | None |
| [sklearn_text_classifier](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/sklearn_text_classifier) | Text classification with [scikit-learn](https://scikit-learn.org/stable/) | ✅ | ❌ | ✅ | None |
| [interactive_substring_matching](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/interactive_substring_matching) | Simple keywords search | ❌ | ✅ | ❌ | None |
| [langchain_search_agent](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/langchain_search_agent) | RAG pipeline with Google Search and [Langchain](https://langchain.com/) | ✅ | ✅ | ✅ | OPENAI_API_KEY, GOOGLE_CSE_ID, GOOGLE_API_KEY |


## Model training
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