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README.md

@@ -0,0 +1,199 @@
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "models/qa_multi_4_retromae_diclp0.05pp0.3_min1",
   "architectures": [
     "KPRModelForBert"
   ],
@@ -30,7 +29,7 @@
   "similarity_function": "dot",
   "similarity_temperature": 1.0,
   "torch_dtype": "float32",
-  "transformers_version": "4.35.2",
+  "transformers_version": "4.55.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "use_entity_position_embeddings": true,

modeling.py

@@ -155,10 +155,12 @@ class EntityFusionLayer(nn.Module):
 
 
 class KPRMixin:
-    def _forward(self, **inputs: Tensor | dict[str, Tensor]) -> Tensor:
+    def _forward(self, **inputs: Tensor | dict[str, Tensor]) -> tuple[Tensor] | tuple[Tensor, Tensor] | ModelOutput:
+        return_dict = inputs.pop("return_dict", True)
+
         if self.training:
-            query_embeddings = self._encode(inputs["queries"])
-            passage_embeddings = self._encode(inputs["passages"])
+            query_embeddings = self.encode(inputs["queries"])
+            passage_embeddings = self.encode(inputs["passages"])
 
             query_embeddings = self._dist_gather_tensor(query_embeddings)
             passage_embeddings = self._dist_gather_tensor(passage_embeddings)
@@ -171,12 +173,19 @@ class KPRMixin:
             ce_target = ce_target * (passage_embeddings.size(0) // query_embeddings.size(0))
             loss = torch.nn.CrossEntropyLoss(reduction="mean")(scores, ce_target)
 
-            return ModelOutput(loss=loss, scores=scores)
+            if return_dict:
+                return ModelOutput(loss=loss, scores=scores)
+            else:
+                return (loss, scores)
 
         else:
-            return self._encode(inputs)
+            sentence_embeddings = self.encode(inputs).unsqueeze(1)
+            if return_dict:
+                return ModelOutput(sentence_embeddings=sentence_embeddings)
+            else:
+                return (sentence_embeddings,)
 
-    def _encode(self, inputs: dict[str, Tensor]) -> Tensor:
+    def encode(self, inputs: dict[str, Tensor]) -> Tensor:
         entity_ids = inputs.pop("entity_ids", None)
         entity_position_ids = inputs.pop("entity_position_ids", None)
         entity_embeds = inputs.pop("entity_embeds", None)
@@ -191,6 +200,8 @@ class KPRMixin:
                 entity_position_ids=entity_position_ids,
                 cls_embeddings=output_embeddings,
             )
+        if self.config.similarity_function == "cosine":
+            output_embeddings = F.normalize(output_embeddings, p=2, dim=-1)
 
         return output_embeddings
 
@@ -205,10 +216,6 @@ class KPRMixin:
         return gathered_tensor
 
     def _compute_similarity(self, query_embeddings: Tensor, passage_embeddings: Tensor) -> Tensor:
-        if self.config.similarity_function == "cosine":
-            query_embeddings = F.normalize(query_embeddings, p=2, dim=-1)
-            passage_embeddings = F.normalize(passage_embeddings, p=2, dim=-1)
-
         return torch.matmul(query_embeddings, passage_embeddings.transpose(-2, -1))