Training in progress, step 1053

README.md

@@ -9,38 +9,32 @@ tags:
 - loss:MultipleNegativesRankingLoss
 base_model: prajjwal1/bert-small
 widget:
-- source_sentence: How would it effect our economy if we ban Chinese products in our
-    country.?
+- source_sentence: How do I calculate IQ?
   sentences:
-  - How would it effect our economy if we ban Chinese products in our country.?
-  - Which cities in India is suitable for part time teaching job where one can prepare
-    for civil services exam?
-  - What is the font used in the desktop version of Instagram for comments?
-- source_sentence: Why do we need Java programming?
+  - What is the easiest way to know my IQ?
+  - How do I calculate not IQ ?
+  - What are some creative and innovative business ideas with less investment in India?
+- source_sentence: How can I learn martial arts in my home?
   sentences:
-  - What is Java? What do I need it for?
-  - Which is the best website to prepare for the Infosys written test?
-  - Can I still get funding to study in UCL (Computer Vision Masters), if I graduated
-    with a BSc in Computer Science with a 2:1 and I have 3 years or web developent
-    expirience?
-- source_sentence: What is capital of china?
+  - How can I learn martial arts by myself?
+  - What are the advantages and disadvantages of investing in gold?
+  - Can people see that I have looked at their pictures on instagram if I am not following
+    them?
+- source_sentence: When Enterprise picks you up do you have to take them back?
   sentences:
-  - How many businesses does Donald Trump own?
-  - What is capital of china?
-  - Where is the capital of China?
-- source_sentence: My Xiaomi Redmi 2 all of a sudden got heated up and then turned
-    off. Now its not charging. What should I do?
+  - Are there any software Training institute in Tuticorin?
+  - When Enterprise picks you up do you have to take them back?
+  - When Enterprise picks you up do them have to take youback?
+- source_sentence: What are some non-capital goods?
   sentences:
-  - My Xiaomi Redmi 2 all of a sudden got heated up and then turned off. Now its not
-    charging. I should Whatdo?
-  - How does the $9 computer work?
-  - My Xiaomi Redmi 2 all of a sudden got heated up and then turned off. Now its not
-    charging. What should I do?
-- source_sentence: How can I get a job in Dubai if I am living in U.S?
+  - What are capital goods?
+  - How is the value of [math]\pi[/math] calculated?
+  - What are some non-capital goods?
+- source_sentence: What is the QuickBooks technical support phone number in New York?
   sentences:
-  - What is the myth behind Mona Lisa smile?
-  - Which is the best series to watch after FRIENDS ?
-  - How can one get a job in Dubai?
+  - What caused the Great Depression?
+  - Can I apply for PR in Canada?
+  - Which is the best QuickBooks Hosting Support Number in New York?
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 ---
@@ -91,12 +85,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("redis/model-b-structured")
+model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'How can I get a job in Dubai if I am living in U.S?',
-    'How can one get a job in Dubai?',
-    'Which is the best series to watch after FRIENDS ?',
+    'What is the QuickBooks technical support phone number in New York?',
+    'Which is the best QuickBooks Hosting Support Number in New York?',
+    'Can I apply for PR in Canada?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -105,9 +99,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[ 1.0000,  0.8904, -0.0302],
-#         [ 0.8904,  1.0000,  0.0224],
-#         [-0.0302,  0.0224,  1.0000]])
+# tensor([[1.0000, 0.8563, 0.0594],
+#         [0.8563, 1.0000, 0.1245],
+#         [0.0594, 0.1245, 1.0000]])
 ```
 
 <!--
@@ -153,18 +147,18 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
 
 * Size: 100,000 training samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* 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: 6 tokens</li><li>mean: 15.51 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.34 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.63 tokens</li><li>max: 128 tokens</li></ul> |
+  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.79 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.68 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.37 tokens</li><li>max: 67 tokens</li></ul> |
 * Samples:
-  | anchor                                                   | positive                                               | negative                                                                          |
-  |:---------------------------------------------------------|:-------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | <code>How do you trace a fake phone number?</code>       | <code>How do you trace a fake phone number?</code>     | <code>How do I trace an internet connection phone number from middle East?</code> |
-  | <code>How do I draw cartoon monsters?</code>             | <code>How do I draw cartoon monsters?</code>           | <code>How do cartoon monsters draw I?</code>                                      |
-  | <code>Do you believe in an afterlife? If so, why?</code> | <code>Do you believe that there's an afterlife?</code> | <code>Do you believe not in an afterlife ? If so , why ?</code>                   |
+  | sentence_0                                                       | sentence_1                                                       | sentence_2                                                                        |
+  |:-----------------------------------------------------------------|:-----------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | <code>Is masturbating bad for boys?</code>                       | <code>Is masturbating bad for boys?</code>                       | <code>How harmful or unhealthy is masturbation?</code>                            |
+  | <code>Does a train engine move in reverse?</code>                | <code>Does a train engine move in reverse?</code>                | <code>Time moves forward, not in reverse. Doesn't that make time a vector?</code> |
+  | <code>What is the most badass thing anyone has ever done?</code> | <code>What is the most badass thing anyone has ever done?</code> | <code>anyone is the most badass thing Whathas ever done?</code>                   |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -177,20 +171,10 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.001
-- `max_steps`: 1170
-- `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
-- `optim`: adamw_torch
-- `ddp_find_unused_parameters`: False
-- `push_to_hub`: True
-- `hub_model_id`: redis/model-b-structured
+- `multi_dataset_batch_sampler`: round_robin
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
@@ -199,24 +183,24 @@ You can finetune this model on your own dataset.
 - `do_predict`: False
 - `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`: 1170
+- `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
@@ -244,9 +228,9 @@ 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
@@ -261,23 +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-b-structured
+- `hub_model_id`: None
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -311,7 +295,7 @@ You can finetune this model on your own dataset.
 - `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`: {}
 
@@ -320,17 +304,15 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss |
 |:------:|:----:|:-------------:|
-| 0.2564 | 100  | 1.0792        |
-| 0.5128 | 200  | 0.2584        |
-| 0.7692 | 300  | 0.1967        |
-| 1.0256 | 400  | 0.1808        |
-| 1.2821 | 500  | 0.1528        |
-| 1.5385 | 600  | 0.1471        |
-| 1.7949 | 700  | 0.1416        |
-| 2.0513 | 800  | 0.1363        |
-| 2.3077 | 900  | 0.1259        |
-| 2.5641 | 1000 | 0.1219        |
-| 2.8205 | 1100 | 0.1212        |
+| 0.3199 | 500  | 0.4294        |
+| 0.6398 | 1000 | 0.1268        |
+| 0.9597 | 1500 | 0.1           |
+| 1.2796 | 2000 | 0.0792        |
+| 1.5995 | 2500 | 0.0706        |
+| 1.9194 | 3000 | 0.0687        |
+| 2.2393 | 3500 | 0.0584        |
+| 2.5592 | 4000 | 0.057         |
+| 2.8791 | 4500 | 0.0581        |
 
 
 ### Framework Versions

eval/Information-Retrieval_evaluation_val_results.csv

@@ -0,0 +1,4 @@
+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-MRR@1,cosine-MRR@5,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
+0,0,0.7522,0.8716,0.8976,0.7522,0.7522,0.29053333333333337,0.8716,0.17951999999999999,0.8976,0.7522,0.8141766666666673,0.8179282539682551,0.8443897738734513,0.8201287535897707
+1.4245014245014245,500,0.8984,0.9626,0.9796,0.8984,0.8984,0.3208666666666667,0.9626,0.19591999999999998,0.9796,0.8984,0.9308266666666667,0.9325202380952388,0.9471587549365493,0.9330120361154303
+2.849002849002849,1000,0.903,0.9652,0.9802,0.903,0.903,0.32173333333333337,0.9652,0.19603999999999996,0.9802,0.903,0.93429,0.93595873015873,0.9497950442756341,0.9364845314523799

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