Update README.md

README.md

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 ---
 license: apache-2.0
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+
+Vision Encoder Model: [timesformer-base-finetuned-k600](https://huggingface.co/facebook/timesformer-base-finetuned-k600) \
+Text Decoder Model: [gpt2](https://huggingface.co/gpt2)
+
+Dataset used: [MSR-VTT](https://paperswithcode.com/dataset/msr-vtt)
+
+#### Results: Epoch 1 finished with average loss: 3.8702
+Epoch 2 finished with average loss: 3.2515
+Epoch 3 finished with average loss: 2.8516
+
+#### Example Inference Code:
+```python
+import av
+import numpy as np
+import torch
+from transformers import AutoImageProcessor, AutoTokenizer, VisionEncoderDecoderModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load pretrained processor, tokenizer, and model
+image_processor = AutoImageProcessor.from_pretrained("notbdq/videogpt")
+tokenizer = AutoTokenizer.from_pretrained("notbdq/videogpt")
+model = VisionEncoderDecoderModel.from_pretrained("notbdq/videogpt").to(device)
+
+video_path = "/kaggle/input/darthvader1/darthvadersurfing.mp4"
+container = av.open(video_path)
+
+# extract evenly spaced frames from video
+seg_len = container.streams.video[0].frames
+clip_len = model.config.encoder.num_frames
+indices = set(np.linspace(0, seg_len, num=clip_len, endpoint=False).astype(np.int64))
+frames = []
+container.seek(0)
+for i, frame in enumerate(container.decode(video=0)):
+    if i in indices:
+        frames.append(frame.to_ndarray(format="rgb24"))
+
+# generate caption
+gen_kwargs = {
+    "max_length": 20, 
+}
+
+pixel_values = image_processor(frames, return_tensors="pt").pixel_values.to(device)
+tokens = model.generate(pixel_values, **gen_kwargs)
+caption = tokenizer.batch_decode(tokens, skip_special_tokens=True)[0]
+print(caption) # man is surfing in the ocean and doing tricks on a surfboard
+```
+
+#### Author Information:
+🐙 [GitHub](https://github.com/notlober) \
+🤝 [LinkedIn](https://www.linkedin.com/in/selahattin-baki-damar-6bb38128a/)