Add new SentenceTransformer model

README.md

@@ -462,6 +462,21 @@ widget:
     JUAN MARÍA ABURTO RIQUE.'
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on intfloat/multilingual-e5-large
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: multilingual e5 large
+      type: multilingual-e5-large
+    metrics:
+    - type: cosine_accuracy
+      value: 0.2175000011920929
+      name: Cosine Accuracy
 ---
 
 # SentenceTransformer based on intfloat/multilingual-e5-large
@@ -525,9 +540,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 1.0000],
-#         [1.0000, 1.0000, 1.0000],
-#         [1.0000, 1.0000, 1.0000]])
+# tensor([[ 1.0000, -0.9967, -0.9967],
+#         [-0.9967,  1.0000,  1.0000],
+#         [-0.9967,  1.0000,  1.0000]])
 ```
 
 <!--
@@ -554,6 +569,19 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Dataset: `multilingual-e5-large`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.2175** |
+
 <!--
 ## Bias, Risks and Limitations
 
@@ -622,8 +650,6 @@ You can finetune this model on your own dataset.
 #### Non-Default Hyperparameters
 
 - `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
 - `warmup_ratio`: 0.1
 - `fp16`: True
 - `batch_sampler`: no_duplicates
@@ -635,8 +661,8 @@ You can finetune this model on your own dataset.
 - `do_predict`: False
 - `eval_strategy`: steps
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
@@ -749,6 +775,17 @@ You can finetune this model on your own dataset.
 
 </details>
 
+### Training Logs
+| Epoch | Step | Training Loss | Validation Loss | multilingual-e5-large_cosine_accuracy |
+|:-----:|:----:|:-------------:|:---------------:|:-------------------------------------:|
+| 0.5   | 100  | 4.1212        | 3.8693          | 0.2325                                |
+| 1.0   | 200  | 3.8258        | 3.8681          | 0.2225                                |
+| 1.5   | 300  | 3.7783        | 3.8678          | 0.2150                                |
+| 2.0   | 400  | 3.8344        | 3.8676          | 0.2325                                |
+| 2.5   | 500  | 3.7929        | 3.8674          | 0.2175                                |
+| 3.0   | 600  | 3.8121        | 3.8674          | 0.2175                                |
+
+
 ### Framework Versions
 - Python: 3.9.7
 - Sentence Transformers: 5.0.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e8c0fe35c993fc833ec6f7438bb28e847dedbbe2bf523028d238194bb69a5364
+oid sha256:71c616df6c24ba89358947095e881f0cba7430855683767390326414888d35b6
 size 2239607176