Update README.md

README.md

@@ -10,11 +10,11 @@ datasets:
 - HuggingFaceM4/Docmatix
 ---
 
-# DSE-Phi3-Docmatix-V1.0
+# DSE-Phi3-Docmatix-V1
 
-DSE-Phi3-Docmatix-V1.0 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval. The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.
+DSE-Phi3-Docmatix-V1 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval. The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.
 
-The model, `Tevatron/dse-phi3-docmatix-v1.0`, is trained using the `Tevatron/docmatix-ir` dataset, a variant of `HuggingFaceM4/Docmatix` specifically adapted for training PDF retrievers with Vision Language Models in open-domain question answering scenarios. For more information on dataset filtering and hard negative mining, refer to the [docmatix-ir](https://huggingface.co/datasets/Tevatron/docmatix-ir/blob/main/README.md) dataset page.
+The model, `Tevatron/dse-phi3-docmatix-v1`, is trained using the `Tevatron/docmatix-ir` dataset, a variant of `HuggingFaceM4/Docmatix` specifically adapted for training PDF retrievers with Vision Language Models in open-domain question answering scenarios. For more information on dataset filtering and hard negative mining, refer to the [docmatix-ir](https://huggingface.co/datasets/Tevatron/docmatix-ir/blob/main/README.md) dataset page.
 
 ## How to Use the Model
 
@@ -24,8 +24,8 @@ The model, `Tevatron/dse-phi3-docmatix-v1.0`, is trained using the `Tevatron/doc
 import torch
 from transformers import AutoProcessor, AutoModelForCausalLM
 
-processor = AutoProcessor.from_pretrained('Tevatron/dse-phi3-docmatix-v1.0', trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained('Tevatron/dse-phi3-docmatix-v1.0', trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False).to('cuda:0')
+processor = AutoProcessor.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False).to('cuda:0')
 
 def get_embedding(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
     sequence_lengths = attention_mask.sum(dim=1) - 1
@@ -53,8 +53,8 @@ import requests
 from io import BytesIO
 
 # URLs of the images
-url1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1.0/resolve/main/animal-llama.png"
-url2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1.0/resolve/main/meta-llama.png"
+url1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1/resolve/main/animal-llama.png"
+url2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v1/resolve/main/meta-llama.png"
 
 # Download and open images
 response1 = requests.get(url1)