Training in progress, step 5000

Information-Retrieval_evaluation_val_results.csv

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precisi
 -1,-1,0.9184,0.97,0.9852,0.9184,0.9184,0.3233333333333333,0.97,0.19703999999999997,0.9852,0.9184,0.9451366666666663,0.9466038095238087,0.9586270476620361,0.946959374340519
 -1,-1,0.829575,0.9048,0.9324,0.829575,0.829575,0.3016,0.9048,0.18648000000000003,0.9324,0.829575,0.8693266666666628,0.873717658730154,0.8957411186558171,0.8757871539962314
 -1,-1,0.829275,0.9051,0.9329,0.829275,0.829275,0.30169999999999997,0.9051,0.18658000000000002,0.9329,0.829275,0.8692179166666618,0.8735753373015815,0.8956869608914538,0.8756452160249361
+-1,-1,0.76415,0.822175,0.84665,0.76415,0.76415,0.2740583333333333,0.822175,0.16933,0.84665,0.76415,0.7953370833333295,0.8000376587301573,0.8195085862842543,0.8034052122492592

README.md

@@ -5,110 +5,38 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:359997
+- dataset_size:100000
 - loss:MultipleNegativesRankingLoss
 base_model: prajjwal1/bert-small
 widget:
-- source_sentence: When do you use Ms. or Mrs.? Is one for a married woman and one
-    for one that's not married? Which one is for what?
+- source_sentence: How do I polish my English skills?
   sentences:
-  - When do you use Ms. or Mrs.? Is one for a married woman and one for one that's
-    not married? Which one is for what?
-  - Nations that do/does otherwise? Which one do I use?
-  - Why don't bikes have a gear indicator?
-- source_sentence: Which ointment is applied to the face of UFC fighters at the commencement
-    of a bout? What does it do?
+  - How can we polish English skills?
+  - Why should I move to Israel as a Jew?
+  - What are vitamins responsible for?
+- source_sentence: Can I use the Kozuka Gothic Pro font as a font-face on my web site?
   sentences:
-  - How can I save a Snapchat video that others posted?
-  - Which ointment is applied to the face of UFC fighters at the commencement of a
-    bout? What does it do?
-  - How do I get the body of a UFC Fighter?
-- source_sentence: Do you love the life you live?
+  - Can I use the Kozuka Gothic Pro font as a font-face on my web site?
+  - Why are Google, Facebook, YouTube and other social networking sites banned in
+    China?
+  - What font is used in Bloomberg Terminal?
+- source_sentence: Is Quora the best Q&A site?
   sentences:
-  - How do I die and make it look like an accident?
-  - Do you love the life you're living?
-  - Why am I not motivated by money?
-- source_sentence: Ordered food on Swiggy 3 days ago.After accepting my money, said
-    no more on Menu! When if ever will I atleast get refund in cr card a/c?
+  - What was the best Quora question ever?
+  - Is Quora the best inquiry site?
+  - Where do I buy Oway hair products online?
+- source_sentence: How can I customize my walking speed on Google Maps?
   sentences:
-  - Is getting to the Tel Aviv airport to catch a 5:30 AM flight very expensive?
-  - What is a cheap healthy diet I can keep the same and eat every day?
-  - Ordered food on Swiggy 3 days ago.After accepting my money, said no more on Menu!
-    When if ever will I atleast get refund in cr card a/c?
-- source_sentence: How do you earn money on Quora?
+  - How do I bring back Google maps icon in my home screen?
+  - How many pages are there in all the Harry Potter books combined?
+  - How can I customize my walking speed on Google Maps?
+- source_sentence: DId something exist before the Big Bang?
   sentences:
-  - What should I do to make my life interesting?
-  - What are the best blogs/sites to follow on fashion?
-  - What is the best way to make money on Quora?
+  - How can I improve my memory problem?
+  - Where can I buy Fairy Tail Manga?
+  - Is there a scientific name for what existed before the Big Bang?
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
-metrics:
-- cosine_accuracy@1
-- cosine_accuracy@3
-- cosine_accuracy@5
-- cosine_precision@1
-- cosine_precision@3
-- cosine_precision@5
-- cosine_recall@1
-- cosine_recall@3
-- cosine_recall@5
-- cosine_ndcg@10
-- cosine_mrr@1
-- cosine_mrr@5
-- cosine_mrr@10
-- cosine_map@100
-model-index:
-- name: SentenceTransformer based on prajjwal1/bert-small
-  results:
-  - task:
-      type: information-retrieval
-      name: Information Retrieval
-    dataset:
-      name: val
-      type: val
-    metrics:
-    - type: cosine_accuracy@1
-      value: 0.764
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.8221
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.84635
-      name: Cosine Accuracy@5
-    - type: cosine_precision@1
-      value: 0.764
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.27403333333333335
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.16927000000000003
-      name: Cosine Precision@5
-    - type: cosine_recall@1
-      value: 0.764
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.8221
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.84635
-      name: Cosine Recall@5
-    - type: cosine_ndcg@10
-      value: 0.8194586562525387
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@1
-      value: 0.764
-      name: Cosine Mrr@1
-    - type: cosine_mrr@5
-      value: 0.7952029166666628
-      name: Cosine Mrr@5
-    - type: cosine_mrr@10
-      value: 0.7999395039682529
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.8032950373722874
-      name: Cosine Map@100
 ---
 
 # SentenceTransformer based on prajjwal1/bert-small
@@ -157,12 +85,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("redis/model-a-baseline")
+model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'How do you earn money on Quora?',
-    'What is the best way to make money on Quora?',
-    'What are the best blogs/sites to follow on fashion?',
+    'DId something exist before the Big Bang?',
+    'Is there a scientific name for what existed before the Big Bang?',
+    'Where can I buy Fairy Tail Manga?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -171,9 +99,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[ 0.9999,  0.9998, -0.0914],
-#         [ 0.9998,  1.0001, -0.0884],
-#         [-0.0914, -0.0884,  1.0000]])
+# tensor([[ 1.0000,  0.7596, -0.0398],
+#         [ 0.7596,  1.0000, -0.0308],
+#         [-0.0398, -0.0308,  1.0000]])
 ```
 
 <!--
@@ -200,32 +128,6 @@ 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
-
-#### Information Retrieval
-
-* Dataset: `val`
-* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
-
-| Metric             | Value      |
-|:-------------------|:-----------|
-| cosine_accuracy@1  | 0.764      |
-| cosine_accuracy@3  | 0.8221     |
-| cosine_accuracy@5  | 0.8464     |
-| cosine_precision@1 | 0.764      |
-| cosine_precision@3 | 0.274      |
-| cosine_precision@5 | 0.1693     |
-| cosine_recall@1    | 0.764      |
-| cosine_recall@3    | 0.8221     |
-| cosine_recall@5    | 0.8464     |
-| **cosine_ndcg@10** | **0.8195** |
-| cosine_mrr@1       | 0.764      |
-| cosine_mrr@5       | 0.7952     |
-| cosine_mrr@10      | 0.7999     |
-| cosine_map@100     | 0.8033     |
-
 <!--
 ## Bias, Risks and Limitations
 
@@ -244,49 +146,23 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 359,997 training samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Size: 100,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 15.46 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.52 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.1 tokens</li><li>max: 128 tokens</li></ul> |
+  |         | sentence_0                                                                        | sentence_1                                                                       | sentence_2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 15.53 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.5 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 128 tokens</li></ul> |
 * Samples:
-  | anchor                                                                          | positive                                                                                             | negative                                                                          |
-  |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | <code>Shall I upgrade my iPhone 5s to iOS 10 final version?</code>              | <code>Should I upgrade an iPhone 5s to iOS 10?</code>                                                | <code>What are the disadvantages and advantages of presidential democracy?</code> |
-  | <code>Is Donald Trump really going to be the president of United States?</code> | <code>Do you think Donald Trump could conceivably be the next President of the United States?</code> | <code>What should we do when we are bored?</code>                                 |
-  | <code>What are real tips to improve work life balance?</code>                   | <code>What are the best ways to create a work life balance?</code>                                   | <code>What are the best sites for college students to earn money online?</code>   |
+  | sentence_0                                                                              | sentence_1                                                                              | sentence_2                                                             |
+  |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
+  | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>How much is the airport tax in bogota?</code>                    |
+  | <code>Which concept is more important: good planning or hard work?</code>               | <code>Which concept is more important: good planning or hard work?</code>               | <code>What is important in life: luck or hard work?</code>             |
+  | <code>What is the most efficient way to make money?</code>                              | <code>How can I make my money make money?</code>                                        | <code>What can one learn about Quantum Mechanics in 10 minutes?</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
-      "scale": 1.0,
-      "similarity_fct": "cos_sim",
-      "gather_across_devices": false
-  }
-  ```
-
-### Evaluation Dataset
-
-#### Unnamed Dataset
-
-* Size: 40,000 evaluation samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | string                                                                           |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 15.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.79 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.8 tokens</li><li>max: 78 tokens</li></ul> |
-* Samples:
-  | anchor                                                                                                            | positive                                                                                                          | negative                                                                                                                                  |
-  |:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why are only few people aware that many dinosaurs had feathers?</code>                                                              |
-  | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>How much did Fox News and conservative leaning media networks stoke the anger that contributed to Donald Trump's popularity?</code> |
-  | <code>How can guys last longer during sex?</code>                                                                 | <code>How do I last longer in sex?</code>                                                                         | <code>How do you get over the fear of death while fighting a war?</code>                                                                  |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 1.0,
+      "scale": 20.0,
       "similarity_fct": "cos_sim",
       "gather_across_devices": false
   }
@@ -295,49 +171,36 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.001
-- `max_steps`: 14060
-- `warmup_ratio`: 0.1
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `fp16`: True
-- `dataloader_drop_last`: True
-- `dataloader_num_workers`: 1
-- `dataloader_prefetch_factor`: 1
-- `load_best_model_at_end`: True
-- `optim`: adamw_torch
-- `ddp_find_unused_parameters`: False
-- `push_to_hub`: True
-- `hub_model_id`: redis/model-a-baseline
-- `eval_on_start`: True
+- `multi_dataset_batch_sampler`: round_robin
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
 
 - `overwrite_output_dir`: False
 - `do_predict`: False
-- `eval_strategy`: steps
+- `eval_strategy`: no
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.001
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 3.0
-- `max_steps`: 14060
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
+- `warmup_ratio`: 0.0
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -365,14 +228,14 @@ You can finetune this model on your own dataset.
 - `tpu_num_cores`: None
 - `tpu_metrics_debug`: False
 - `debug`: []
-- `dataloader_drop_last`: True
-- `dataloader_num_workers`: 1
-- `dataloader_prefetch_factor`: 1
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
 - `past_index`: -1
 - `disable_tqdm`: False
 - `remove_unused_columns`: True
 - `label_names`: None
-- `load_best_model_at_end`: True
+- `load_best_model_at_end`: False
 - `ignore_data_skip`: False
 - `fsdp`: []
 - `fsdp_min_num_params`: 0
@@ -382,23 +245,23 @@ You can finetune this model on your own dataset.
 - `parallelism_config`: None
 - `deepspeed`: None
 - `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
+- `optim`: adamw_torch_fused
 - `optim_args`: None
 - `adafactor`: False
 - `group_by_length`: False
 - `length_column_name`: length
 - `project`: huggingface
 - `trackio_space_id`: trackio
-- `ddp_find_unused_parameters`: False
+- `ddp_find_unused_parameters`: None
 - `ddp_bucket_cap_mb`: None
 - `ddp_broadcast_buffers`: False
 - `dataloader_pin_memory`: True
 - `dataloader_persistent_workers`: False
 - `skip_memory_metrics`: True
 - `use_legacy_prediction_loop`: False
-- `push_to_hub`: True
+- `push_to_hub`: False
 - `resume_from_checkpoint`: None
-- `hub_model_id`: redis/model-a-baseline
+- `hub_model_id`: None
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -425,167 +288,32 @@ You can finetune this model on your own dataset.
 - `neftune_noise_alpha`: None
 - `optim_target_modules`: None
 - `batch_eval_metrics`: False
-- `eval_on_start`: True
+- `eval_on_start`: False
 - `use_liger_kernel`: False
 - `liger_kernel_config`: None
 - `eval_use_gather_object`: False
 - `average_tokens_across_devices`: True
 - `prompts`: None
 - `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
+- `multi_dataset_batch_sampler`: round_robin
 - `router_mapping`: {}
 - `learning_rate_mapping`: {}
 
 </details>
 
 ### Training Logs
-<details><summary>Click to expand</summary>
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3199 | 500  | 0.2284        |
+| 0.6398 | 1000 | 0.0571        |
+| 0.9597 | 1500 | 0.0486        |
+| 1.2796 | 2000 | 0.0378        |
+| 1.5995 | 2500 | 0.0367        |
+| 1.9194 | 3000 | 0.0338        |
+| 2.2393 | 3500 | 0.0327        |
+| 2.5592 | 4000 | 0.0285        |
+| 2.8791 | 4500 | 0.0285        |
 
-| Epoch  | Step  | Training Loss | Validation Loss | val_cosine_ndcg@10 |
-|:------:|:-----:|:-------------:|:---------------:|:------------------:|
-| 0      | 0     | -             | 5.9519          | 0.8045             |
-| 0.0711 | 100   | 5.9667        | 5.7035          | 0.8414             |
-| 0.1422 | 200   | 5.7144        | 5.4232          | 0.8556             |
-| 0.2134 | 300   | 5.5593        | 5.3409          | 0.8496             |
-| 0.2845 | 400   | 5.4925        | 5.3198          | 0.8407             |
-| 0.3556 | 500   | 5.457         | 5.3155          | 0.8334             |
-| 0.4267 | 600   | 5.4353        | 5.3145          | 0.8287             |
-| 0.4979 | 700   | 5.4205        | 5.3114          | 0.8248             |
-| 0.5690 | 800   | 5.4107        | 5.3092          | 0.8222             |
-| 0.6401 | 900   | 5.4           | 5.3071          | 0.8192             |
-| 0.7112 | 1000  | 5.3927        | 5.3057          | 0.8176             |
-| 0.7824 | 1100  | 5.3867        | 5.3042          | 0.8173             |
-| 0.8535 | 1200  | 5.3815        | 5.3008          | 0.8171             |
-| 0.9246 | 1300  | 5.3757        | 5.2991          | 0.8163             |
-| 0.9957 | 1400  | 5.3711        | 5.2976          | 0.8156             |
-| 1.0669 | 1500  | 5.3671        | 5.2953          | 0.8160             |
-| 1.1380 | 1600  | 5.3641        | 5.2942          | 0.8162             |
-| 1.2091 | 1700  | 5.3611        | 5.2941          | 0.8166             |
-| 1.2802 | 1800  | 5.3588        | 5.2927          | 0.8167             |
-| 1.3514 | 1900  | 5.3549        | 5.2916          | 0.8162             |
-| 1.4225 | 2000  | 5.3542        | 5.2915          | 0.8162             |
-| 1.4936 | 2100  | 5.3512        | 5.2903          | 0.8164             |
-| 1.5647 | 2200  | 5.3502        | 5.2898          | 0.8166             |
-| 1.6358 | 2300  | 5.3479        | 5.2898          | 0.8162             |
-| 1.7070 | 2400  | 5.346         | 5.2889          | 0.8161             |
-| 1.7781 | 2500  | 5.3444        | 5.2881          | 0.8161             |
-| 1.8492 | 2600  | 5.3431        | 5.2879          | 0.8164             |
-| 1.9203 | 2700  | 5.3414        | 5.2876          | 0.8165             |
-| 1.9915 | 2800  | 5.34          | 5.2869          | 0.8166             |
-| 2.0626 | 2900  | 5.3384        | 5.2865          | 0.8166             |
-| 2.1337 | 3000  | 5.3365        | 5.2858          | 0.8163             |
-| 2.2048 | 3100  | 5.3356        | 5.2859          | 0.8168             |
-| 2.2760 | 3200  | 5.3346        | 5.2857          | 0.8163             |
-| 2.3471 | 3300  | 5.3338        | 5.2850          | 0.8166             |
-| 2.4182 | 3400  | 5.3324        | 5.2851          | 0.8171             |
-| 2.4893 | 3500  | 5.3323        | 5.2845          | 0.8170             |
-| 2.5605 | 3600  | 5.33          | 5.2837          | 0.8172             |
-| 2.6316 | 3700  | 5.3297        | 5.2841          | 0.8174             |
-| 2.7027 | 3800  | 5.3281        | 5.2838          | 0.8173             |
-| 2.7738 | 3900  | 5.3273        | 5.2835          | 0.8175             |
-| 2.8450 | 4000  | 5.3259        | 5.2829          | 0.8176             |
-| 2.9161 | 4100  | 5.3258        | 5.2834          | 0.8182             |
-| 2.9872 | 4200  | 5.3245        | 5.2827          | 0.8181             |
-| 3.0583 | 4300  | 5.3229        | 5.2821          | 0.8184             |
-| 3.1294 | 4400  | 5.3224        | 5.2820          | 0.8181             |
-| 3.2006 | 4500  | 5.3217        | 5.2819          | 0.8179             |
-| 3.2717 | 4600  | 5.3212        | 5.2820          | 0.8181             |
-| 3.3428 | 4700  | 5.3208        | 5.2814          | 0.8186             |
-| 3.4139 | 4800  | 5.3197        | 5.2819          | 0.8181             |
-| 3.4851 | 4900  | 5.3196        | 5.2815          | 0.8183             |
-| 3.5562 | 5000  | 5.3189        | 5.2811          | 0.8184             |
-| 3.6273 | 5100  | 5.3183        | 5.2807          | 0.8181             |
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-| 9.9573 | 14000 | 5.2964        | 5.2751          | 0.8195             |
-
-</details>
 
 ### Framework Versions
 - Python: 3.10.18

eval/Information-Retrieval_evaluation_val_results.csv

@@ -462,3 +462,147 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precisi
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