Create huggingface_library_transformer.md

huggingface_library_transformer.md

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+The Hugging Face Transformers library absolutely supports models for images, audio/voice, and video — not just text.
+
+Here’s how it works:
+
+## ✅ Hugging Face Transformers Supports Multiple Data Types
+## 1. Text (NLP)
+
+Models: BERT, GPT-2, T5, LLaMA, Mistral
+Class: AutoModelForSequenceClassification, AutoModelForCausalLM, etc.
+
+## 2. Images (Vision Transformers)
+
+Models: ViT, DeiT, BEiT, Swin, ConvNeXT, DINO, MAE
+Class: AutoModelForImageClassification
+
+Example:
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224")
+
+image = Image.open("cat.jpg")
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+pred = outputs.logits.argmax(-1)
+print(pred)
+```
+
+## 3. Audio / Speech
+
+Models: Wav2Vec2, Whisper, HuBERT, SpeechT5
+Class: AutoModelForSpeechRecognition
+
+Example:
+```python
+from transformers import AutoProcessor, AutoModelForSpeechRecognition
+import torch
+import soundfile as sf
+
+processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
+model = AutoModelForSpeechRecognition.from_pretrained("facebook/wav2vec2-base-960h")
+
+speech, rate = sf.read("speech.wav")
+inputs = processor(speech, sampling_rate=rate, return_tensors="pt")
+logits = model(**inputs).logits
+pred_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(pred_ids)
+print(transcription)
+```
+## 4. Video
+
+Models: TimeSformer, VideoMAE
+Class: AutoModelForVideoClassification
+
+Example:
+```python
+from transformers import AutoImageProcessor, AutoModelForVideoClassification
+import torch, av
+
+processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base")
+model = AutoModelForVideoClassification.from_pretrained("MCG-NJU/videomae-base")
+
+# (Pseudo example - you'd extract video frames first)
+frames = [...]  
+inputs = processor(frames, return_tensors="pt")
+outputs = model(**inputs)
+print(outputs.logits.argmax(-1))
+```
+## 5. Multimodal (Text + Image / Video)
+
+Models: CLIP, BLIP, ViLT
+Class: AutoModel depending on task
+
+Example (CLIP):
+```python
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+image = Image.open("dog.jpg")
+inputs = processor(text=["a dog", "a cat"], images=image, return_tensors="pt", padding=True)
+
+outputs = model(**inputs)
+logits = outputs.logits_per_image
+print(logits.softmax(dim=-1))
+```
+## 🔹 Conclusion
+
+➡️ Yes, Hugging Face Transformers includes models for text, images, audio/voice, video, and multimodal tasks.
+It started with NLP but has expanded into computer vision, speech, and multimodal AI.