Create all-types-of-transformers.md

all-types-of-transformers.md

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+Perfect ✅ Here’s a ready-to-use table of Hugging Face Transformers models for different data types, with their exact model IDs you can load directly.
+
+## 🔹 Hugging Face Transformer Models by Data Type
+| Data Type                           | Model Name                  | Hugging Face ID                               |
+| ----------------------------------- | --------------------------- | --------------------------------------------- |
+| **Text (NLP)**                      | BERT                        | `bert-base-uncased`                           |
+|                                     | GPT-2                       | `gpt2`                                        |
+|                                     | T5                          | `t5-small`                                    |
+|                                     | DistilBERT                  | `distilbert-base-uncased`                     |
+| **Image (Vision)**                  | Vision Transformer (ViT)    | `google/vit-base-patch16-224`                 |
+|                                     | DeiT (Data-efficient ViT)   | `facebook/deit-base-distilled-patch16-224`    |
+|                                     | BEiT                        | `microsoft/beit-base-patch16-224-pt22k-ft22k` |
+|                                     | Swin Transformer            | `microsoft/swin-tiny-patch4-window7-224`      |
+| **Audio / Speech**                  | Wav2Vec2 (ASR)              | `facebook/wav2vec2-base-960h`                 |
+|                                     | Whisper (ASR + Translation) | `openai/whisper-small`                        |
+|                                     | HuBERT (speech features)    | `facebook/hubert-base-ls960`                  |
+|                                     | SpeechT5 (TTS + STT)        | `microsoft/speecht5_asr`                      |
+| **Video**                           | TimeSformer                 | `facebook/timesformer-base-finetuned-k400`    |
+|                                     | VideoMAE                    | `MCG-NJU/videomae-base`                       |
+| **Multimodal (Text + Image/Video)** | CLIP                        | `openai/clip-vit-base-patch32`                |
+|                                     | BLIP (captioning, VQA)      | `Salesforce/blip-image-captioning-base`       |
+|                                     | ViLT (vision-language)      | `dandelin/vilt-b32-finetuned-coco`            |
+
+🔹 Example Usage (generic)
+```python
+from transformers import AutoProcessor, AutoModel, AutoTokenizer
+
+# Example: Load a vision transformer
+model_id = "google/vit-base-patch16-224"
+model = AutoModel.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+
+print("Loaded:", model_id)
+```
+
+✅ This table should save you time: just copy the model ID and plug it into AutoModel / AutoProcessor / AutoTokenizer depending on the task.
+
+👉 Do you want me to also write a full script that auto-detects the data type (text, image, audio, video) and loads the right Hugging Face model? That way, you can just pass "text", "image", "audio", "video" and it works automatically.