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yNilay
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colbert-personal-context

Sentence Similarity
Safetensors
sentence-transformers
PyLate
lfm2
ColBERT
feature-extraction
Generated from Trainer
dataset_size:5191
loss:Contrastive
Eval Results (legacy)
Model card Files
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Add new ColBERT model

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Files changed (12) hide show
  1. 1_Dense/config.json +7 -0
  2. 1_Dense/model.safetensors +3 -0
  3. README.md +540 -0
  4. chat_template.jinja +7 -0
  5. config.json +57 -0
  6. config_sentence_transformers.json +53 -0
  7. model.safetensors +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +17 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +4101 -0
1_Dense/config.json ADDED
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+ {
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+ "in_features": 1024,
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+ "out_features": 128,
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+ "bias": false,
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+ "activation_function": "torch.nn.modules.linear.Identity",
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+ "use_residual": false
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+ }
1_Dense/model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d0791e9b31f0a813074496e369bfa05ddf1ad9b4bd84171fc550fc95141af1f8
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+ size 524376
README.md ADDED
@@ -0,0 +1,540 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - ColBERT
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+ - PyLate
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:2595
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+ - loss:Contrastive
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+ base_model: LiquidAI/LFM2-ColBERT-350M
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+ pipeline_tag: sentence-similarity
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+ library_name: PyLate
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: PyLate model based on LiquidAI/LFM2-ColBERT-350M
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+ results:
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+ - task:
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+ type: col-berttriplet
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+ name: Col BERTTriplet
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ metrics:
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+ - type: accuracy
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+ value: 0.8996540307998657
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+ name: Accuracy
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+ ---
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+
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+ # PyLate model based on LiquidAI/LFM2-ColBERT-350M
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+
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+ This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [LiquidAI/LFM2-ColBERT-350M](https://huggingface.co/LiquidAI/LFM2-ColBERT-350M). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** PyLate model
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+ - **Base model:** [LiquidAI/LFM2-ColBERT-350M](https://huggingface.co/LiquidAI/LFM2-ColBERT-350M) <!-- at revision 3cccd22d874924de3d56640167f1aa056c0c6809 -->
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+ - **Document Length:** 512 tokens
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+ - **Query Length:** 32 tokens
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+ - **Output Dimensionality:** 128 tokens
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+ - **Similarity Function:** MaxSim
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
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+ - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
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+ - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ ColBERT(
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+ (0): Transformer({'max_seq_length': 511, 'do_lower_case': False, 'architecture': 'Lfm2Model'})
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+ (1): Dense({'in_features': 1024, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
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+ )
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+ ```
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+
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+ ## Usage
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+ First install the PyLate library:
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+
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+ ```bash
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+ pip install -U pylate
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+ ```
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+
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+ ### Retrieval
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+
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+ Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
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+
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+ #### Indexing documents
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+
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+ Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
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+
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+ ```python
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+ from pylate import indexes, models, retrieve
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+
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+ # Step 1: Load the ColBERT model
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+ model = models.ColBERT(
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+ model_name_or_path="yNilay/colbert-personal-context",
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+ )
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+
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+ # Step 2: Initialize the PLAID index
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+ index = indexes.PLAID(
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+ index_folder="pylate-index",
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+ index_name="index",
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+ override=True, # This overwrites the existing index if any
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+ )
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+
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+ # Step 3: Encode the documents
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+ documents_ids = ["1", "2", "3"]
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+ documents = ["document 1 text", "document 2 text", "document 3 text"]
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+
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+ documents_embeddings = model.encode(
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+ documents,
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+ batch_size=32,
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+ is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
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+ show_progress_bar=True,
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+ )
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+
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+ # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
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+ index.add_documents(
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+ documents_ids=documents_ids,
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+ documents_embeddings=documents_embeddings,
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+ )
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+ ```
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+
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+ Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
112
+
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+ ```python
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+ # To load an index, simply instantiate it with the correct folder/name and without overriding it
115
+ index = indexes.PLAID(
116
+ index_folder="pylate-index",
117
+ index_name="index",
118
+ )
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+ ```
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+
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+ #### Retrieving top-k documents for queries
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+
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+ Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
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+ To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
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+
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+ ```python
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+ # Step 1: Initialize the ColBERT retriever
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+ retriever = retrieve.ColBERT(index=index)
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+
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+ # Step 2: Encode the queries
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+ queries_embeddings = model.encode(
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+ ["query for document 3", "query for document 1"],
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+ batch_size=32,
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+ is_query=True, # # Ensure that it is set to False to indicate that these are queries
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+ show_progress_bar=True,
136
+ )
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+
138
+ # Step 3: Retrieve top-k documents
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+ scores = retriever.retrieve(
140
+ queries_embeddings=queries_embeddings,
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+ k=10, # Retrieve the top 10 matches for each query
142
+ )
143
+ ```
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+
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+ ### Reranking
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+ If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
147
+
148
+ ```python
149
+ from pylate import rank, models
150
+
151
+ queries = [
152
+ "query A",
153
+ "query B",
154
+ ]
155
+
156
+ documents = [
157
+ ["document A", "document B"],
158
+ ["document 1", "document C", "document B"],
159
+ ]
160
+
161
+ documents_ids = [
162
+ [1, 2],
163
+ [1, 3, 2],
164
+ ]
165
+
166
+ model = models.ColBERT(
167
+ model_name_or_path="yNilay/colbert-personal-context",
168
+ )
169
+
170
+ queries_embeddings = model.encode(
171
+ queries,
172
+ is_query=True,
173
+ )
174
+
175
+ documents_embeddings = model.encode(
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+ documents,
177
+ is_query=False,
178
+ )
179
+
180
+ reranked_documents = rank.rerank(
181
+ documents_ids=documents_ids,
182
+ queries_embeddings=queries_embeddings,
183
+ documents_embeddings=documents_embeddings,
184
+ )
185
+ ```
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+
187
+ <!--
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+ ### Direct Usage (Transformers)
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+
190
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
192
+ </details>
193
+ -->
194
+
195
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
198
+ You can finetune this model on your own dataset.
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+
200
+ <details><summary>Click to expand</summary>
201
+
202
+ </details>
203
+ -->
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+
205
+ <!--
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+ ### Out-of-Scope Use
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+
208
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
209
+ -->
210
+
211
+ ## Evaluation
212
+
213
+ ### Metrics
214
+
215
+ #### Col BERTTriplet
216
+
217
+ * Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
218
+
219
+ | Metric | Value |
220
+ |:-------------|:-----------|
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+ | **accuracy** | **0.8997** |
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+
223
+ <!--
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+ ## Bias, Risks and Limitations
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+
226
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
229
+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
235
+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 2,595 training samples
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+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | query | positive | negative |
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+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 23 tokens</li><li>mean: 31.83 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 32.0 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 32.0 tokens</li><li>max: 32 tokens</li></ul> |
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+ * Samples:
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+ | query | positive | negative |
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+ |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>Should I approve Barry's recommendation to switch from Jet Aviation to EJM for the plane management? He mentioned EJM is more expensive, but I need to remember the tax implications we discussed - something about foreign travel preponderance affecting how we can depreciate the aircraft and whether switching to charter flying would help with the accelerated depreciation rules. Can you remind me of the key points from our discussions about the profit model, the Apollo charter situation, and what the aviation tax counsel advised about depreciation?</code> | <code>Subject: Gulfstream V - offmarket, sleeper aircraft<br><br><br>---<br><br>From: Jeffrey Epstein <jeevacation@gmail.com><br>To: Eric Roth<br>Date: Jun 24, 2011, at 7:56 PM<br><br>eric, price for a re-rag , new seats , g-v , paint and interior, plus approx time after choosing fabrics etc.<br><br>---<br><br>From: Eric Roth<br>To: Jeffrey Epstein <jeevacation@gmail.com><br>Date: Fri, Jun 24, 2011 at 8:20 PM<br><br>Any photos? Serial number?<br><br>Sent from my iPhone<br><br>---<br><br>From: Jeffrey Epstein <jeevacation@gmail.com><br>To: Eric Roth<br>Date: Saturday, June 25, 2011 12:29</code> | <code>Subject: Re: Summers withdraws from Fed race<br><br><br>---<br><br>From: Jeffrey Epstein [jeevacation@gmail.com]<br>To: ehud barak<br>Date: 9/16/2013 11:15:03 AM<br><br>yes the good side is now he is free to have fun and a life . LIsa the wife is very happy as she did not want to live in two cities. tell you more when i see you<br><br>---<br><br>From: chud barak<br>Date: Mon, Sep 16, 2013 at 7:12 AM<br><br>A major blow to a friend. Lest BO takes him to replace JL. Now he'll probably be free in the next few days. EB</code> |
253
+ | <code>I'm doing a media interview next week and want to be ready. Can you remind me what happened the last time reporters came asking about the Clinton/Epstein story? Who reached out and what did we end up saying? Also, what did Kathy advise me about dealing with media back in March 2016 when that Patterson book was coming out?</code> | <code>Subject: Fw: Meet?<br><br><br>---<br><br>From: Martin G. Weinberg [martinweinberg@i...]<br>To: jeevacation@gmail.com<br>Date: 1/21/2016 12:18:58 AM<br><br><br><br>---<br><br>From: Mosk, Matthew<br>Date: Wednesday, January 20, 2016 6:08 PM<br><br>Mr. Weinberg —<br><br>I am a reporter with the ABC News investigative unit. We are gathering background research on the major candidates for President.<br><br>Seeing that both former President Clinton and Mr. Trump have been mentioned in connection to the Jeffrey Epstein case, I am interested in better understanding the case</code> | <code>Subject: Re: Results<br><br><br>---<br><br>From: Al seckel [aseckel@yahoo.com]<br>To: Jeffrey Epstein [jeevacation@gmail.com]<br>Date: 12/16/2010 1:20:06 PM<br><br>you can call as 1 am obviously (iim<br><br>---<br><br>From: Jeffrey Epstein <jeevacation@gmail.com><br>To: Al seckel<br>Date: Thu, December 16, 2010 5:01:25 AM<br><br>I was never told never, that there was a 10k fee per month,, you inittaly said the project would take 20.. then<br>another 10. then another 10. .<br><br>---<br><br>From: Al seckel<br>To: Jeffrey<br>Date: Wed, Dec 15, 2010 at 12:36 PM<br><br>The bots did the s</code> |
254
+ | <code>What's the latest on Lisa's Poetry in America project? Last I heard she was trying to get the Woody Allen footage released and the TV series was supposed to launch in 2018. Did that happen? And where do we stand on the additional funding for the Modernism period she wanted to shoot?</code> | <code>Subject: Re: Highlight<br><br><br>---<br><br>From: Lisa New<br>Date: Tue, Sep 23, 2014 at 6:17 PM<br><br>That was fun- definitely the highlight of a day that was not without other interesting moments<br><br>Don't know which was better-- the wholly unexpected surprise reminder (by a prime minister) of my<br>boyhood boyfriend from 1976( who was in high school, who lived with his doting parents), or , getting creamed on<br>my producer's budget but then offered the opportunity to profess poetry for production funds . There may be<br>someone as good </code> | <code>Subject: RE: AVGO<br><br><br>---<br><br>From: jeffrey E. <jeevacation@gmail.com><br>To: Paul Barrett<br>Date: Friday, July 13, 2018 7:11 AM<br><br>Im not angry, sorry, it is sad. it is apparent that you have no experience trading. we both made a mistake. . the spy trades were the results of a rank amatuer.. you took a postion on the direction of the market. ok. you decided to play it extra short time and short. . why? becaue the options are cheaper. ? knowing full well that time premium was running. . first silly move. . I allowed as</code> |
255
+ * Loss: <code>pylate.losses.contrastive.Contrastive</code>
256
+
257
+ ### Evaluation Dataset
258
+
259
+ #### Unnamed Dataset
260
+
261
+
262
+ * Size: 289 evaluation samples
263
+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
264
+ * Approximate statistics based on the first 289 samples:
265
+ | | query | positive | negative |
266
+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
267
+ | type | string | string | string |
268
+ | details | <ul><li>min: 23 tokens</li><li>mean: 31.88 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 32.0 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 32.0 tokens</li><li>max: 32 tokens</li></ul> |
269
+ * Samples:
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+ | query | positive | negative |
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+ |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>What's the latest on that whole Cohen situation and the legal mess with Trump's team? I remember Reid was worried about McGahn getting indicted and something about taking the fifth - did Cohen end up being the bigger problem?</code> | <code>Subject: Re: <br><br><br>---<br><br>From: jeffrey E.<br>To: Weingarten, Reid<br>Date: Tuesday, August 21, 2018 5:57 PM<br><br>avenatti civil suit now goes front and center? I guess trump just refuses ? how are you feeling?<br><br>---<br><br>From: Weingarten, Reid<br>To: jeffrey E. [jeevacation@gmail.com]<br>Date: Tuesday, August 21, 2018 6:34 PM<br><br>Quite a day...have a feeling cohen is just beginning to make trouble...where are you?<br><br>---<br><br>From: Jeffrey E.<br>To: Weingarten, Reid<br>Date: Tuesday, August 21, 2018 6:38 PM<br><br>david pecker ? what happends to him<br><br>-</code> | <code>Subject: Oscar diary<br><br><br>---<br><br>From: Peggy Siegal<br>To: Stephanie<br>Date: Friday, March 04, 2011 6:12 PM<br><br>grueling work have finally paid off. King George VI and Harvey Weinstein now share the journey of a single man who triumphs over adversity.<br><br>The Governor’s ball, held above the Kodak Theater, recreates the Mocambo-Ciro’s niteries of the 1930’s and 40’s, using a palette of teal and white. Three bands alternate musical styles as hungry guests wolf down wolfgang Puck’s delicious food. The winners triumphantly sac</code> |
273
+ | <code>Should I pay my financial advisor the $40M he's demanding to fix my office mess, or just cut ties? He's been incredibly critical of my team—says Brad's incompetent, Joslin needs to go, Castrucci is useless, and the accounting is a disaster. But he also found $600M in tax savings and has delivered for years. He wants to fire half my staff and rebuild everything. He forwarded me that email about having breakfast with Reid Weingarten after the Qataris came over that night—Reid thought Turkey was using ISIS as a proxy against the Kurds. Should I trust his judgment on my team or is he just after the money?</code> | <code>Subject: Fwd: fact checking questions for New York Magazine story<br><br><br>---<br><br>From: Yablon, Alex<br>To: jeevacation@gmail.com<br>Date: Tue, Mar 31, 2015 at 11:23 AM<br><br>Hi Jeffrey, <br>Sorry for the phone tag. In case it's easier to respond by email, I have put my questions below. I'm at my desk and should be <br>here until 5, and should be free on my cell phone after 6:30. <br>Best, <br>Alex<br><br>-what is the square footage of your Manhattan home? <br>-do you work on a laptop from your dining room, with a large white board for notes and s</code> | <code>Subject: Per your point about Trump/Aramco/Ponzi<br><br><br>---<br><br>From: Thomas Jr., Landon<br>To: Jeffrey Epstein [jeevacation@gmail.com]<br>Date: 5/30/2017 12:51:12 PM<br><br>https://www.fairobserver.com/region/middle_east_north_africa/donald-trump-saudi-arabia-oil-aramco-news-60922/?utm campaign=Daily%2o0Fringe%20&utm source=hs email&utm medium=email&utm content=52487236& hsenc=p2ANqtz--UTwt81DzZpk6zyw34g9GdPZPoZwsqwM128tJRlurvCO743YcshoDCCjkHnAUT_ aPi33tdrUG6_Paht5ufCkN48qfxw& hsmi=52487236</code> |
274
+ | <code>What was Michael Wolff's advice about dealing with the Patterson book and should I still follow his media strategy recommendations from back in 2016?</code> | <code>Subject: Patterson<br><br><br>---<br><br>From: Michael Wolff<br>To: Jeffrey Epstein [jeevacation@gmail.com]<br>Date: 3/18/2016 3:39:39 PM<br><br>A few things to think about:<br><br>If the Patterson book is being published in August, that presents some time frame issues. You would not be able to do a competing book or documentary before then. This is not to say that they shouldn't be pursued. In a sense, better that they know what Patterson's position is and, a year from now say, be able to counter it. I have some thoughts on book and doc t</code> | <code>Subject: Re: for your reading pleasure.. though maybe not<br><br><br>---<br><br>From: Lawrence Krauss<br>To: jeffrey E. [jeevacation@gmail.com], Lawrence Krauss<br>Date: Thu, Dec 15, 2016 at 7:05 PM<br><br>http://www.newyorker.com/tech/elements/donald-trumps-war-on-science?intcid=mod-latest<br>http://www.nytimes.com/2016/12/13/opinion/rick-perry-is-the-wrong-choice-for-energy-secretary.html?action=click&pgtype=Homepage&clickSource=story-heading&module=opinion-c-col-left-region&region=opinion-c-col-left-region&WT.nav=opinion-c-col-left-r</code> |
275
+ * Loss: <code>pylate.losses.contrastive.Contrastive</code>
276
+
277
+ ### Training Hyperparameters
278
+ #### Non-Default Hyperparameters
279
+
280
+ - `eval_strategy`: epoch
281
+ - `per_device_train_batch_size`: 32
282
+ - `learning_rate`: 3e-06
283
+ - `num_train_epochs`: 5
284
+ - `warmup_ratio`: 0.1
285
+ - `bf16`: True
286
+ - `load_best_model_at_end`: True
287
+
288
+ #### All Hyperparameters
289
+ <details><summary>Click to expand</summary>
290
+
291
+ - `overwrite_output_dir`: False
292
+ - `do_predict`: False
293
+ - `eval_strategy`: epoch
294
+ - `prediction_loss_only`: True
295
+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 8
297
+ - `per_gpu_train_batch_size`: None
298
+ - `per_gpu_eval_batch_size`: None
299
+ - `gradient_accumulation_steps`: 1
300
+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
302
+ - `learning_rate`: 3e-06
303
+ - `weight_decay`: 0.0
304
+ - `adam_beta1`: 0.9
305
+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
307
+ - `max_grad_norm`: 1.0
308
+ - `num_train_epochs`: 5
309
+ - `max_steps`: -1
310
+ - `lr_scheduler_type`: linear
311
+ - `lr_scheduler_kwargs`: {}
312
+ - `warmup_ratio`: 0.1
313
+ - `warmup_steps`: 0
314
+ - `log_level`: passive
315
+ - `log_level_replica`: warning
316
+ - `log_on_each_node`: True
317
+ - `logging_nan_inf_filter`: True
318
+ - `save_safetensors`: True
319
+ - `save_on_each_node`: False
320
+ - `save_only_model`: False
321
+ - `restore_callback_states_from_checkpoint`: False
322
+ - `no_cuda`: False
323
+ - `use_cpu`: False
324
+ - `use_mps_device`: False
325
+ - `seed`: 42
326
+ - `data_seed`: None
327
+ - `jit_mode_eval`: False
328
+ - `use_ipex`: False
329
+ - `bf16`: True
330
+ - `fp16`: False
331
+ - `fp16_opt_level`: O1
332
+ - `half_precision_backend`: auto
333
+ - `bf16_full_eval`: False
334
+ - `fp16_full_eval`: False
335
+ - `tf32`: None
336
+ - `local_rank`: 0
337
+ - `ddp_backend`: None
338
+ - `tpu_num_cores`: None
339
+ - `tpu_metrics_debug`: False
340
+ - `debug`: []
341
+ - `dataloader_drop_last`: False
342
+ - `dataloader_num_workers`: 0
343
+ - `dataloader_prefetch_factor`: None
344
+ - `past_index`: -1
345
+ - `disable_tqdm`: False
346
+ - `remove_unused_columns`: True
347
+ - `label_names`: None
348
+ - `load_best_model_at_end`: True
349
+ - `ignore_data_skip`: False
350
+ - `fsdp`: []
351
+ - `fsdp_min_num_params`: 0
352
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
353
+ - `fsdp_transformer_layer_cls_to_wrap`: None
354
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
355
+ - `parallelism_config`: None
356
+ - `deepspeed`: None
357
+ - `label_smoothing_factor`: 0.0
358
+ - `optim`: adamw_torch_fused
359
+ - `optim_args`: None
360
+ - `adafactor`: False
361
+ - `group_by_length`: False
362
+ - `length_column_name`: length
363
+ - `ddp_find_unused_parameters`: None
364
+ - `ddp_bucket_cap_mb`: None
365
+ - `ddp_broadcast_buffers`: False
366
+ - `dataloader_pin_memory`: True
367
+ - `dataloader_persistent_workers`: False
368
+ - `skip_memory_metrics`: True
369
+ - `use_legacy_prediction_loop`: False
370
+ - `push_to_hub`: False
371
+ - `resume_from_checkpoint`: None
372
+ - `hub_model_id`: None
373
+ - `hub_strategy`: every_save
374
+ - `hub_private_repo`: None
375
+ - `hub_always_push`: False
376
+ - `hub_revision`: None
377
+ - `gradient_checkpointing`: False
378
+ - `gradient_checkpointing_kwargs`: None
379
+ - `include_inputs_for_metrics`: False
380
+ - `include_for_metrics`: []
381
+ - `eval_do_concat_batches`: True
382
+ - `fp16_backend`: auto
383
+ - `push_to_hub_model_id`: None
384
+ - `push_to_hub_organization`: None
385
+ - `mp_parameters`:
386
+ - `auto_find_batch_size`: False
387
+ - `full_determinism`: False
388
+ - `torchdynamo`: None
389
+ - `ray_scope`: last
390
+ - `ddp_timeout`: 1800
391
+ - `torch_compile`: False
392
+ - `torch_compile_backend`: None
393
+ - `torch_compile_mode`: None
394
+ - `include_tokens_per_second`: False
395
+ - `include_num_input_tokens_seen`: False
396
+ - `neftune_noise_alpha`: None
397
+ - `optim_target_modules`: None
398
+ - `batch_eval_metrics`: False
399
+ - `eval_on_start`: False
400
+ - `use_liger_kernel`: False
401
+ - `liger_kernel_config`: None
402
+ - `eval_use_gather_object`: False
403
+ - `average_tokens_across_devices`: False
404
+ - `prompts`: None
405
+ - `batch_sampler`: batch_sampler
406
+ - `multi_dataset_batch_sampler`: proportional
407
+ - `router_mapping`: {}
408
+ - `learning_rate_mapping`: {}
409
+
410
+ </details>
411
+
412
+ ### Training Logs
413
+ | Epoch | Step | Training Loss | Validation Loss | accuracy |
414
+ |:-------:|:-------:|:-------------:|:---------------:|:--------:|
415
+ | 0.1220 | 10 | 8.082 | - | - |
416
+ | 0.2439 | 20 | 5.3715 | - | - |
417
+ | 0.3659 | 30 | 3.4194 | - | - |
418
+ | 0.4878 | 40 | 2.9049 | - | - |
419
+ | 0.6098 | 50 | 3.1343 | - | - |
420
+ | 0.7317 | 60 | 2.9416 | - | - |
421
+ | 0.8537 | 70 | 3.1289 | - | - |
422
+ | 0.9756 | 80 | 2.8016 | - | - |
423
+ | 0 | 0 | - | - | 0.7993 |
424
+ | 1.0 | 82 | - | 1.5435 | - |
425
+ | 1.0976 | 90 | 2.4153 | - | - |
426
+ | 1.2195 | 100 | 2.6782 | - | - |
427
+ | 1.3415 | 110 | 2.6065 | - | - |
428
+ | 1.4634 | 120 | 2.5967 | - | - |
429
+ | 1.5854 | 130 | 2.505 | - | - |
430
+ | 1.7073 | 140 | 2.6173 | - | - |
431
+ | 1.8293 | 150 | 2.5698 | - | - |
432
+ | 1.9512 | 160 | 2.7154 | - | - |
433
+ | 0 | 0 | - | - | 0.8720 |
434
+ | 2.0 | 164 | - | 1.3534 | - |
435
+ | 2.0732 | 170 | 2.2109 | - | - |
436
+ | 2.1951 | 180 | 2.1319 | - | - |
437
+ | 2.3171 | 190 | 2.1895 | - | - |
438
+ | 2.4390 | 200 | 2.1939 | - | - |
439
+ | 2.5610 | 210 | 2.2309 | - | - |
440
+ | 2.6829 | 220 | 2.4645 | - | - |
441
+ | 2.8049 | 230 | 2.3214 | - | - |
442
+ | 2.9268 | 240 | 2.3195 | - | - |
443
+ | 0 | 0 | - | - | 0.9066 |
444
+ | 3.0 | 246 | - | 1.3047 | - |
445
+ | 3.0488 | 250 | 1.7162 | - | - |
446
+ | 3.1707 | 260 | 1.8789 | - | - |
447
+ | 3.2927 | 270 | 1.8849 | - | - |
448
+ | 3.4146 | 280 | 1.7345 | - | - |
449
+ | 3.5366 | 290 | 2.1667 | - | - |
450
+ | 3.6585 | 300 | 1.9041 | - | - |
451
+ | 3.7805 | 310 | 1.9473 | - | - |
452
+ | 3.9024 | 320 | 1.7976 | - | - |
453
+ | 0 | 0 | - | - | 0.8962 |
454
+ | **4.0** | **328** | **-** | **1.2892** | **-** |
455
+ | 4.0244 | 330 | 1.6116 | - | - |
456
+ | 4.1463 | 340 | 1.6325 | - | - |
457
+ | 4.2683 | 350 | 1.86 | - | - |
458
+ | 4.3902 | 360 | 1.5819 | - | - |
459
+ | 4.5122 | 370 | 1.7592 | - | - |
460
+ | 4.6341 | 380 | 1.6431 | - | - |
461
+ | 4.7561 | 390 | 1.57 | - | - |
462
+ | 4.8780 | 400 | 1.6919 | - | - |
463
+ | 5.0 | 410 | 1.5353 | - | - |
464
+ | 0 | 0 | - | - | 0.8997 |
465
+ | 5.0 | 410 | - | 1.2919 | - |
466
+
467
+ * The bold row denotes the saved checkpoint.
468
+
469
+ ### Framework Versions
470
+ - Python: 3.11.12
471
+ - Sentence Transformers: 5.1.1
472
+ - PyLate: 1.4.0
473
+ - Transformers: 4.56.2
474
+ - PyTorch: 2.9.0+cu128
475
+ - Accelerate: 1.13.0
476
+ - Datasets: 4.8.4
477
+ - Tokenizers: 0.22.2
478
+
479
+
480
+ ## Citation
481
+
482
+ ### BibTeX
483
+
484
+ #### Sentence Transformers
485
+ ```bibtex
486
+ @inproceedings{reimers-2019-sentence-bert,
487
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
488
+ author = "Reimers, Nils and Gurevych, Iryna",
489
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
490
+ month = "11",
491
+ year = "2019",
492
+ publisher = "Association for Computational Linguistics",
493
+ url = "https://arxiv.org/abs/1908.10084"
494
+ }
495
+ ```
496
+
497
+ #### PyLate
498
+ ```bibtex
499
+ @inproceedings{DBLP:conf/cikm/ChaffinS25,
500
+ author = {Antoine Chaffin and
501
+ Rapha{"{e}}l Sourty},
502
+ editor = {Meeyoung Cha and
503
+ Chanyoung Park and
504
+ Noseong Park and
505
+ Carl Yang and
506
+ Senjuti Basu Roy and
507
+ Jessie Li and
508
+ Jaap Kamps and
509
+ Kijung Shin and
510
+ Bryan Hooi and
511
+ Lifang He},
512
+ title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
513
+ booktitle = {Proceedings of the 34th {ACM} International Conference on Information
514
+ and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
515
+ 10-14, 2025},
516
+ pages = {6334--6339},
517
+ publisher = {{ACM}},
518
+ year = {2025},
519
+ url = {https://github.com/lightonai/pylate},
520
+ doi = {10.1145/3746252.3761608},
521
+ }
522
+ ```
523
+
524
+ <!--
525
+ ## Glossary
526
+
527
+ *Clearly define terms in order to be accessible across audiences.*
528
+ -->
529
+
530
+ <!--
531
+ ## Model Card Authors
532
+
533
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
534
+ -->
535
+
536
+ <!--
537
+ ## Model Card Contact
538
+
539
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
540
+ -->
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+ "use_cache": true,
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+ "use_pos_enc": true,
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+ }
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The diff for this file is too large to render. See raw diff
 
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