Update README.md

README.md

@@ -1,323 +1,323 @@
----
-language:
-- en
-library_name: transformers
-license: apache-2.0
-pipeline_tag: image-text-to-text
-tags:
-- Sentence Similarity
-- Embedding
-- zero-shot-image-classification
-- video-text-to-text
----
-
-# UME-R1-7B
-
-## Model Summary
-
-The model has undergone a cold-start SFT stage and an RL stage of training, and is capable of embedding text, images, multiple images, and videos. In particular, UME-R1 can generate either discriminative or generative embeddings as needed, and the generative embeddings possess the potential for test-time scaling.
-
-- **Repository:** [UME-R1](https://github.com/DeepLearnXMU/UME-R1)
-- **Paper:** [UME-R1]()
-
-## Train/Eval Data
- - Train data: https://huggingface.co/datasets/zhibinlan/UME-sft-train
- - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-V2
-
-
-
-
-## Model Performance
-UME-R1 significantly outperforms discriminative embeddings and can provide discriminative or generative representations as needed. Its oracle performance—selecting the best between discriminative and generative—far exceeds using either mode alone.
-
-
-<img src="./figures/main_result.png" alt="MMEB-V2" width="1200" height="auto">
-<!-- ![MMEB-V2](./figures/main_result.png) -->
-
-In addition, UME-R1 can produce improved embedding representations through repeated sampling, indicating that generative embeddings also hold strong promise for inference-time scaling. 
-
-<img src="./figures/scaling.png" alt="pass@k" width="1200" height="auto">
-
-### Quick Start
-
-First clone our github
-```bash
-git clone https://github.com/DeepLearnXMU/UME-R1
-cd UME-R1
-bash setup.sh
-```
-
-Below, we provide simple examples to show how to use UME-R1 with 🤗 Transformers.
-
-Example of obtaining generative embeddings:
-
-```python
-from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
-from qwen_vl_utils import process_vision_info
-import torch
-
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "zhibinlan/UME-R1-7B",
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2",
-    device_map="cuda:0",
-)
-
-processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-7B")
-
-prompt = '''Represent the above input text, images, videos, or any combination of the three as embeddings. 
-First output the thinking process in <think> </think> tags and then summarize the entire input in a word or sentence. 
-Finally, use the <gen_emb> tag to represent the entire input.'''
-
-
-
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "assets/example.jpg",
-            },
-            {"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n" + prompt},
-        ],
-    }
-]
-
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to(model.device)
-
-# Inference: Generation of the output
-generated_output = model.generate(**inputs, max_new_tokens=8192, output_hidden_states=True, return_dict_in_generate=True, use_cache=True)
-# Post-process the output
-generated_ids = generated_output.sequences
-hidden_states = generated_output.hidden_states
-
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-
-def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
-
-    embedding_idx = []
-    for i, out_ids in enumerate(generated_ids_trimmed):
-        embed_exist = False
-        for j in range(len(out_ids) - 1, -1, -1):
-            if out_ids[j] == EMBEDDING_TOKEN_ID:
-                embedding_idx.append(j + 1)
-                embed_exist = True
-                break
-        if not embed_exist:
-            embedding_idx.append(-1)
-
-    return embedding_idx
-
-def normalize_reps(reps):
-    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
-    return reps
-
-# Get the last hidden state of the <gen_emb> token
-embedding_idx = get_embedding_idx(generated_ids_trimmed, processor.tokenizer.get_vocab()["<gen_emb>"])
-embedding_reps = hidden_states[embedding_idx[0]][-1].squeeze(1)
-
-# Normalize the representations
-embedding_reps = normalize_reps(embedding_reps)
-
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
-)
-```
-
-<details>
-<summary>Example of obtaining discriminative embeddings</summary>
-
-```python
-from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
-from qwen_vl_utils import process_vision_info
-import torch
-
-pretrained_path = "zhibinlan/UME-R1-7B"
-
-# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    pretrained_path,
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2",
-    device_map="cuda:0",
-)
-
-# default processor
-processor = AutoProcessor.from_pretrained(pretrained_path)
-
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "UME-R1/assets/example.jpg",
-            },
-            {"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n"},
-        ],
-    }
-]
-
-# Preparation for inference
-text = processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-
-image_inputs, video_inputs = process_vision_info(messages)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    padding=True,
-    return_tensors="pt",
-)
-inputs = inputs.to(model.device)
-
-def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
-
-    embedding_idx = []
-    # Search from the last token forward
-    for i, out_ids in enumerate(generated_ids_trimmed):
-        embed_exist = False
-        for j in range(len(out_ids) - 1, -1, -1):
-            if out_ids[j] == EMBEDDING_TOKEN_ID:
-                embedding_idx.append(j)
-                embed_exist = True
-                break
-        if not embed_exist:
-            embedding_idx.append(-1)
-
-    return embedding_idx
-
-def normalize_reps(reps):
-    # Normalize the representations
-    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
-    return reps
-
-output = model(**inputs, output_hidden_states=True, return_dict=True)
-hidden_states = output.hidden_states[-1][0]
-# print("output.hidden_states shape: ", hidden_states.shape)
-embedding_idx = get_embedding_idx(inputs['input_ids'], processor.tokenizer.get_vocab()["<disc_emb>"])
-
-# Get the last hidden state of the <gen_emb> token
-embedding_reps = hidden_states[embedding_idx[0]]
-
-# Normalize the representations
-embedding_reps = normalize_reps(embedding_reps)
-```
-
-</details>
-
-<details>
-<summary>Multi image inference</summary>
-
-```python
-# Messages containing multiple images and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "image": "file:///path/to/image1.jpg"},
-            {"type": "image", "image": "file:///path/to/image2.jpg"},
-            {"type": "text", "text": "Represent the given images."},
-        ],
-    }
-]
-```
-
-</details>
-
-<details>
-<summary>Video inference</summary>
-
-```python
-# Messages containing a images list as a video and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": [
-                    "file:///path/to/frame1.jpg",
-                    "file:///path/to/frame2.jpg",
-                    "file:///path/to/frame3.jpg",
-                    "file:///path/to/frame4.jpg",
-                ],
-            },
-            {"type": "text", "text": "Represent this video."},
-        ],
-    }
-]
-
-# Messages containing a local video path and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "file:///path/to/video1.mp4",
-                "max_pixels": 360 * 420,
-                "fps": 1.0,
-            },
-            {"type": "text", "text": "Represent this video."},
-        ],
-    }
-]
-
-# Messages containing a video url and a text query
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "https://path/to/video.mp4",
-                "min_pixels": 4 * 28 * 28,
-                "max_pixels": 256 * 28 * 28,
-                "total_pixels": 20480 * 28 * 28,
-            },
-            {"type": "text", "text": "Represent this video."},
-        ],
-    }
-]
-image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
-inputs = processor(
-    text=[text],
-    images=image_inputs,
-    videos=video_inputs,
-    fps=fps,
-    padding=True,
-    return_tensors="pt",
-    **video_kwargs,
-)
-```
-
-</details>
-
-
-For more usage tips, please refer to our [Github page](https://github.com/DeepLearnXMU/UME-R1).
-
-
-## Citation
-If you find our work helpful, feel free to give us a cite.
-
-```
+---
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- Sentence Similarity
+- Embedding
+- zero-shot-image-classification
+- video-text-to-text
+---
+
+# UME-R1-7B
+
+## Model Summary
+
+The model has undergone a cold-start SFT stage and an RL stage of training, and is capable of embedding text, images, multiple images, and videos. In particular, UME-R1 can generate either discriminative or generative embeddings as needed, and the generative embeddings possess the potential for test-time scaling.
+
+- **Repository:** [UME-R1](https://github.com/DeepLearnXMU/UME-R1)
+- **Paper:** [UME-R1](https://arxiv.org/abs/2511.00405)
+
+## Train/Eval Data
+ - Train data: https://huggingface.co/datasets/zhibinlan/UME-sft-train
+ - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-V2
+
+
+
+
+## Model Performance
+UME-R1 significantly outperforms discriminative embeddings and can provide discriminative or generative representations as needed. Its oracle performance—selecting the best between discriminative and generative—far exceeds using either mode alone.
+
+
+<img src="./figures/main_result.png" alt="MMEB-V2" width="1200" height="auto">
+<!-- ![MMEB-V2](./figures/main_result.png) -->
+
+In addition, UME-R1 can produce improved embedding representations through repeated sampling, indicating that generative embeddings also hold strong promise for inference-time scaling. 
+
+<img src="./figures/scaling.png" alt="pass@k" width="1200" height="auto">
+
+### Quick Start
+
+First clone our github
+```bash
+git clone https://github.com/DeepLearnXMU/UME-R1
+cd UME-R1
+bash setup.sh
+```
+
+Below, we provide simple examples to show how to use UME-R1 with 🤗 Transformers.
+
+Example of obtaining generative embeddings:
+
+```python
+from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "zhibinlan/UME-R1-7B",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="cuda:0",
+)
+
+processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-7B")
+
+prompt = '''Represent the above input text, images, videos, or any combination of the three as embeddings. 
+First output the thinking process in <think> </think> tags and then summarize the entire input in a word or sentence. 
+Finally, use the <gen_emb> tag to represent the entire input.'''
+
+
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "assets/example.jpg",
+            },
+            {"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n" + prompt},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to(model.device)
+
+# Inference: Generation of the output
+generated_output = model.generate(**inputs, max_new_tokens=8192, output_hidden_states=True, return_dict_in_generate=True, use_cache=True)
+# Post-process the output
+generated_ids = generated_output.sequences
+hidden_states = generated_output.hidden_states
+
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+
+def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
+
+    embedding_idx = []
+    for i, out_ids in enumerate(generated_ids_trimmed):
+        embed_exist = False
+        for j in range(len(out_ids) - 1, -1, -1):
+            if out_ids[j] == EMBEDDING_TOKEN_ID:
+                embedding_idx.append(j + 1)
+                embed_exist = True
+                break
+        if not embed_exist:
+            embedding_idx.append(-1)
+
+    return embedding_idx
+
+def normalize_reps(reps):
+    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
+    return reps
+
+# Get the last hidden state of the <gen_emb> token
+embedding_idx = get_embedding_idx(generated_ids_trimmed, processor.tokenizer.get_vocab()["<gen_emb>"])
+embedding_reps = hidden_states[embedding_idx[0]][-1].squeeze(1)
+
+# Normalize the representations
+embedding_reps = normalize_reps(embedding_reps)
+
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
+)
+```
+
+<details>
+<summary>Example of obtaining discriminative embeddings</summary>
+
+```python
+from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+pretrained_path = "zhibinlan/UME-R1-7B"
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    pretrained_path,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="cuda:0",
+)
+
+# default processor
+processor = AutoProcessor.from_pretrained(pretrained_path)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "UME-R1/assets/example.jpg",
+            },
+            {"type": "text", "text": "Represent the given image with the following question: What is in the image?\n<disc_emb>\n"},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to(model.device)
+
+def get_embedding_idx(generated_ids_trimmed, EMBEDDING_TOKEN_ID):
+
+    embedding_idx = []
+    # Search from the last token forward
+    for i, out_ids in enumerate(generated_ids_trimmed):
+        embed_exist = False
+        for j in range(len(out_ids) - 1, -1, -1):
+            if out_ids[j] == EMBEDDING_TOKEN_ID:
+                embedding_idx.append(j)
+                embed_exist = True
+                break
+        if not embed_exist:
+            embedding_idx.append(-1)
+
+    return embedding_idx
+
+def normalize_reps(reps):
+    # Normalize the representations
+    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
+    return reps
+
+output = model(**inputs, output_hidden_states=True, return_dict=True)
+hidden_states = output.hidden_states[-1][0]
+# print("output.hidden_states shape: ", hidden_states.shape)
+embedding_idx = get_embedding_idx(inputs['input_ids'], processor.tokenizer.get_vocab()["<disc_emb>"])
+
+# Get the last hidden state of the <gen_emb> token
+embedding_reps = hidden_states[embedding_idx[0]]
+
+# Normalize the representations
+embedding_reps = normalize_reps(embedding_reps)
+```
+
+</details>
+
+<details>
+<summary>Multi image inference</summary>
+
+```python
+# Messages containing multiple images and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "Represent the given images."},
+        ],
+    }
+]
+```
+
+</details>
+
+<details>
+<summary>Video inference</summary>
+
+```python
+# Messages containing a images list as a video and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": [
+                    "file:///path/to/frame1.jpg",
+                    "file:///path/to/frame2.jpg",
+                    "file:///path/to/frame3.jpg",
+                    "file:///path/to/frame4.jpg",
+                ],
+            },
+            {"type": "text", "text": "Represent this video."},
+        ],
+    }
+]
+
+# Messages containing a local video path and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "max_pixels": 360 * 420,
+                "fps": 1.0,
+            },
+            {"type": "text", "text": "Represent this video."},
+        ],
+    }
+]
+
+# Messages containing a video url and a text query
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "https://path/to/video.mp4",
+                "min_pixels": 4 * 28 * 28,
+                "max_pixels": 256 * 28 * 28,
+                "total_pixels": 20480 * 28 * 28,
+            },
+            {"type": "text", "text": "Represent this video."},
+        ],
+    }
+]
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    fps=fps,
+    padding=True,
+    return_tensors="pt",
+    **video_kwargs,
+)
+```
+
+</details>
+
+
+For more usage tips, please refer to our [Github page](https://github.com/DeepLearnXMU/UME-R1).
+
+
+<!-- ## Citation
+If you find our work helpful, feel free to give us a cite. -->
+
+```
 ```