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checkpoint-1000/README.md

@@ -13,31 +13,31 @@ tags:
 - dataset_size:74247
 - loss:AnglELoss
 widget:
-- source_sentence: hand of fatima decanter
-  sentences:
-  - matte concealer
-  - car refresher spray
-  - bread
-- source_sentence: intensive antiwrinkle face cream
+- source_sentence: paste
   sentences:
   - bread
-  - bread
   - car refresher spray
-- source_sentence: aromatic coffee capsules
+  - hot drinks mug
+- source_sentence: shoulder strap accessory for bags
   sentences:
-  - bread
-  - car refresher spray
-  - bread
-- source_sentence: side fringe jacket
+  - home scent
+  - ball
+  - one outer pocket cross bag
+- source_sentence: deep fried mozzarella sticks
   sentences:
-  - car refresher spray
-  - car refresher spray
+  - one outer pocket cross bag
+  - insect killer
+  - stir fry vegetables bowl
+- source_sentence: metal flower set tableaus
+  sentences:
+  - home scent
   - home scent
-- source_sentence: pastelcolored cookies
+  - hygiene
+- source_sentence: basic choker
   sentences:
-  - matte concealer
-  - rice
-  - bisk
+  - hygiene
+  - unisex sweatshirt
+  - unisex sweatshirt
 ---
 
 # all-MiniLM-L6-v6-pair_score
@@ -90,9 +90,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'pastelcolored cookies',
-    'bisk',
-    'rice',
+    'basic choker',
+    'unisex sweatshirt',
+    'unisex sweatshirt',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -149,7 +149,7 @@ You can finetune this model on your own dataset.
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
 - `learning_rate`: 2e-05
-- `num_train_epochs`: 2
+- `num_train_epochs`: 15
 - `warmup_ratio`: 0.1
 - `fp16`: True
 
@@ -173,7 +173,7 @@ You can finetune this model on your own dataset.
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
-- `num_train_epochs`: 2
+- `num_train_epochs`: 15
 - `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
@@ -274,16 +274,16 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss |
 |:------:|:----:|:-------------:|
-| 0.1721 | 100  | 8.9697        |
-| 0.3442 | 200  | 2.6119        |
-| 0.5164 | 300  | 0.9351        |
-| 0.6885 | 400  | 0.7049        |
-| 0.8606 | 500  | 0.394         |
-| 1.0327 | 600  | 0.6792        |
-| 1.2048 | 700  | 0.715         |
-| 1.3769 | 800  | 0.5655        |
-| 1.5491 | 900  | 0.5354        |
-| 1.7212 | 1000 | 0.6299        |
+| 0.1721 | 100  | 10.8697       |
+| 0.3442 | 200  | 9.1125        |
+| 0.5164 | 300  | 6.8873        |
+| 0.6885 | 400  | 3.1124        |
+| 0.8606 | 500  | 1.0882        |
+| 1.0327 | 600  | 0.869         |
+| 1.2048 | 700  | 0.6952        |
+| 1.3769 | 800  | 0.5522        |
+| 1.5491 | 900  | 0.5184        |
+| 1.7212 | 1000 | 0.3996        |
 
 
 ### Framework Versions

checkpoint-1000/model.safetensors

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checkpoint-1000/optimizer.pt

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checkpoint-1000/rng_state.pth

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checkpoint-1000/scheduler.pt

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checkpoint-1000/trainer_state.json

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   "logging_steps": 100,
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+  "num_train_epochs": 15,
   "save_steps": 500,
   "stateful_callbacks": {
     "TrainerControl": {

checkpoint-1000/training_args.bin

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