Training in progress, step 305, checkpoint

checkpoint-305/1_AdvancedWeightedPooling/config.json

@@ -0,0 +1,12 @@
+{
+  "embed_dim": 768,
+  "num_heads": 4,
+  "dropout": 0.025,
+  "bias": true,
+  "gate_min": 0.05,
+  "gate_max": 0.95,
+  "gate_dropout": 0.01,
+  "dropout_gate_open": 0.075,
+  "dropout_gate_close": 0.05,
+  "CLS_self_attn": 0
+}

checkpoint-305/1_AdvancedWeightedPooling/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68b599379e5b06ef871fb82d51b71a5d4b321a2416ef815c4c2bbb4dc6f7ed7f
+size 18940723

checkpoint-305/README.md

@@ -0,0 +1,1158 @@
+---
+base_model: microsoft/deberta-v3-small
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:32500
+- loss:GISTEmbedLoss
+widget:
+- source_sentence: Fish hatch into larvae that are different from the adult form of
+    species.
+  sentences:
+  - Fish hatch into larvae that are different from the adult form of?
+  - amphibians hatch from eggs
+  - A solenoid or coil wrapped around iron or certain other metals can form a(n) electromagnet.
+- source_sentence: About 200 countries and territories have reported coronavirus cases
+    in 2020 .
+  sentences:
+  - All-Time Olympic Games Medal Tally Analysis Home > Events > Olympics > Summer
+    > Medal Tally > All-Time All-Time Olympic Games Medal Tally (Summer Olympics)
+    Which country is the most successful at he Olympic Games? Here are the top ranked
+    countries in terms of total medals won when all of the summer Games are considered
+    (including the 2016 Rio Games). There are two tables presented, the first just
+    lists the top countries based on the total medals won, the second table factors
+    in how many Olympic Games the country appeared, averaging the total number of
+    medals per Olympiad. A victory in a team sport is counted as one medal. The USA
+    Has Won the Most Medals The US have clearly won the most gold medals and the most
+    medals overall, more than doubling the next ranked country (these figures include
+    medals won in Rio 2016). Second placed USSR had fewer appearances at the Olympics,
+    and actually won more medals on average (see the 2nd table). The top 10 includes
+    one country no longer in existence (the Soviet Union), so their medal totals will
+    obviously not increase, however China is expected to continue a rapid rise up
+    the ranks. With the addition of the 2016 data, China has moved up from 11th (in
+    2008) to 9th (2012) to 7th (2016). The country which has attended the most games
+    without a medal is Monaco (20 Olympic Games), the country which has won the most
+    medals without winning a gold medal is Malaysia (0 gold, 7 silver, 4 bronze).
+    rank
+  - An example of a reproductive behavior is salmon returning to their birthplace
+    to lay their eggs
+  - more than 664,000 cases of COVID-19 have been reported in over 190 countries and
+    territories , resulting in approximately 30,800 deaths .
+- source_sentence: The wave on a guitar string is transverse. the sound wave rattles
+    a sheet of paper in a direction that shows the sound wave is what?
+  sentences:
+  - A Honda motorcycle parked in a grass driveway
+  - In Panama tipping is a question of rewarding good service rather than an obligation.
+    Restaurant bills don't include gratuities; adding 10% is customary. Bellhops and
+    maids expect tips only in more expensive hotels, and $1–$2 per bag is the norm.
+    You should also give a tip of up to $10 per day to tour guides.
+  - Figure 16.33 The wave on a guitar string is transverse. The sound wave rattles
+    a sheet of paper in a direction that shows the sound wave is longitudinal.
+- source_sentence: The thermal production of a stove is generically used for
+  sentences:
+  - In total , 28 US victims were killed , while Viet Cong losses were killed 345
+    and a further 192 estimated killed .
+  - a stove generates heat for cooking usually
+  - A teenager has been charged over an incident in which a four-year-old girl was
+    hurt when she was hit in the face by a brick thrown through a van window.
+- source_sentence: can sweet potatoes cause itching?
+  sentences:
+  - 'People with a true potato allergy may react immediately after touching, peeling,
+    or eating potatoes. Symptoms may vary from person to person, but typical symptoms
+    of a potato allergy include: rhinitis, including itchy or stinging eyes, a runny
+    or stuffy nose, and sneezing.'
+  - riding a bike does not cause pollution
+  - "Dilation occurs when cell walls relax.. An aneurysm is a dilation, or bubble,\
+    \ that occurs in the wall of an artery. \n an artery can be relaxed by dilation"
+model-index:
+- name: SentenceTransformer based on microsoft/deberta-v3-small
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.2749904272806095
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.31159390381099095
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.2923996087310511
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.3095556181083969
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.2934483033082174
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.3115817314678925
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.27496363262371837
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.31138581044552094
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.2934483033082174
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.31159390381099095
+      name: Spearman Max
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: allNLI dev
+      type: allNLI-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.67578125
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9452645182609558
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.512
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.8565204739570618
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.39143730886850153
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.7398843930635838
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.4264736612515921
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.67578125
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 726.30615234375
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.512
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 658.1103515625
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.39143730886850153
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.7398843930635838
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.42647535250956575
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.67578125
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 201.49061584472656
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.5107692307692308
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 417.52728271484375
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.3480083857442348
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.9595375722543352
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.4252213828672732
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.67578125
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 9.171283721923828
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.512
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 14.84876823425293
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.39143730886850153
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.7398843930635838
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.4264736612515921
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.67578125
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 726.30615234375
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.512
+      name: Max F1
+    - type: max_f1_threshold
+      value: 658.1103515625
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.39143730886850153
+      name: Max Precision
+    - type: max_recall
+      value: 0.9595375722543352
+      name: Max Recall
+    - type: max_ap
+      value: 0.42647535250956575
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Qnli dev
+      type: Qnli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.634765625
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8508153557777405
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.6505636070853462
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7770615816116333
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.5246753246753246
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8559322033898306
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6461335447626624
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.634765625
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 653.7443237304688
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.6505636070853462
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 597.0731811523438
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.5246753246753246
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.8559322033898306
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.6461682282377894
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.6328125
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 331.46282958984375
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.6501650165016502
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 404.6050109863281
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.5324324324324324
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8347457627118644
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.6431949026371255
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.634765625
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 15.141305923461914
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.6505636070853462
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 18.50943946838379
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.5246753246753246
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8559322033898306
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.6461382925406688
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.634765625
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 653.7443237304688
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.6505636070853462
+      name: Max F1
+    - type: max_f1_threshold
+      value: 597.0731811523438
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.5324324324324324
+      name: Max Precision
+    - type: max_recall
+      value: 0.8559322033898306
+      name: Max Recall
+    - type: max_ap
+      value: 0.6461682282377894
+      name: Max Ap
+---
+
+# SentenceTransformer based on microsoft/deberta-v3-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
+  (1): AdvancedWeightedPooling(
+    (alpha_dropout_layer): Dropout(p=0.01, inplace=False)
+    (gate_dropout_layer): Dropout(p=0.05, inplace=False)
+    (linear_cls_pj): Linear(in_features=768, out_features=768, bias=True)
+    (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
+    (linear_mean_pj): Linear(in_features=768, out_features=768, bias=True)
+    (linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
+    (mha): MultiheadAttention(
+      (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
+    )
+    (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_pjCls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_pjMean): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+  )
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest3-step1-checkpoints-tmp")
+# Run inference
+sentences = [
+    'can sweet potatoes cause itching?',
+    'People with a true potato allergy may react immediately after touching, peeling, or eating potatoes. Symptoms may vary from person to person, but typical symptoms of a potato allergy include: rhinitis, including itchy or stinging eyes, a runny or stuffy nose, and sneezing.',
+    'riding a bike does not cause pollution',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.275      |
+| **spearman_cosine** | **0.3116** |
+| pearson_manhattan   | 0.2924     |
+| spearman_manhattan  | 0.3096     |
+| pearson_euclidean   | 0.2934     |
+| spearman_euclidean  | 0.3116     |
+| pearson_dot         | 0.275      |
+| spearman_dot        | 0.3114     |
+| pearson_max         | 0.2934     |
+| spearman_max        | 0.3116     |
+
+#### Binary Classification
+* Dataset: `allNLI-dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6758     |
+| cosine_accuracy_threshold    | 0.9453     |
+| cosine_f1                    | 0.512      |
+| cosine_f1_threshold          | 0.8565     |
+| cosine_precision             | 0.3914     |
+| cosine_recall                | 0.7399     |
+| cosine_ap                    | 0.4265     |
+| dot_accuracy                 | 0.6758     |
+| dot_accuracy_threshold       | 726.3062   |
+| dot_f1                       | 0.512      |
+| dot_f1_threshold             | 658.1104   |
+| dot_precision                | 0.3914     |
+| dot_recall                   | 0.7399     |
+| dot_ap                       | 0.4265     |
+| manhattan_accuracy           | 0.6758     |
+| manhattan_accuracy_threshold | 201.4906   |
+| manhattan_f1                 | 0.5108     |
+| manhattan_f1_threshold       | 417.5273   |
+| manhattan_precision          | 0.348      |
+| manhattan_recall             | 0.9595     |
+| manhattan_ap                 | 0.4252     |
+| euclidean_accuracy           | 0.6758     |
+| euclidean_accuracy_threshold | 9.1713     |
+| euclidean_f1                 | 0.512      |
+| euclidean_f1_threshold       | 14.8488    |
+| euclidean_precision          | 0.3914     |
+| euclidean_recall             | 0.7399     |
+| euclidean_ap                 | 0.4265     |
+| max_accuracy                 | 0.6758     |
+| max_accuracy_threshold       | 726.3062   |
+| max_f1                       | 0.512      |
+| max_f1_threshold             | 658.1104   |
+| max_precision                | 0.3914     |
+| max_recall                   | 0.9595     |
+| **max_ap**                   | **0.4265** |
+
+#### Binary Classification
+* Dataset: `Qnli-dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6348     |
+| cosine_accuracy_threshold    | 0.8508     |
+| cosine_f1                    | 0.6506     |
+| cosine_f1_threshold          | 0.7771     |
+| cosine_precision             | 0.5247     |
+| cosine_recall                | 0.8559     |
+| cosine_ap                    | 0.6461     |
+| dot_accuracy                 | 0.6348     |
+| dot_accuracy_threshold       | 653.7443   |
+| dot_f1                       | 0.6506     |
+| dot_f1_threshold             | 597.0732   |
+| dot_precision                | 0.5247     |
+| dot_recall                   | 0.8559     |
+| dot_ap                       | 0.6462     |
+| manhattan_accuracy           | 0.6328     |
+| manhattan_accuracy_threshold | 331.4628   |
+| manhattan_f1                 | 0.6502     |
+| manhattan_f1_threshold       | 404.605    |
+| manhattan_precision          | 0.5324     |
+| manhattan_recall             | 0.8347     |
+| manhattan_ap                 | 0.6432     |
+| euclidean_accuracy           | 0.6348     |
+| euclidean_accuracy_threshold | 15.1413    |
+| euclidean_f1                 | 0.6506     |
+| euclidean_f1_threshold       | 18.5094    |
+| euclidean_precision          | 0.5247     |
+| euclidean_recall             | 0.8559     |
+| euclidean_ap                 | 0.6461     |
+| max_accuracy                 | 0.6348     |
+| max_accuracy_threshold       | 653.7443   |
+| max_f1                       | 0.6506     |
+| max_f1_threshold             | 597.0732   |
+| max_precision                | 0.5324     |
+| max_recall                   | 0.8559     |
+| **max_ap**                   | **0.6462** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 32,500 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 29.6 tokens</li><li>max: 369 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 58.01 tokens</li><li>max: 437 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                             | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>The song ‘Fashion for His Love’ by Lady Gaga is a tribute to which late fashion designer?</code>                                                                                                                                                                                                | <code>Fashion Of His Love by Lady Gaga Songfacts Fashion Of His Love by Lady Gaga Songfacts Songfacts Gaga explained in a tweet that this track from her Born This Way Special Edition album is about the late Alexander McQueen. The fashion designer committed suicide by hanging on February 11, 2010 and Gaga was deeply affected by the tragic death of McQueen, who was a close personal friend. That same month, she performed at the 2010 Brit Awards wearing one of his couture creations and she also paid tribute to her late friend by setting the date on the prison security cameras in her Telephone video as the same day that McQueen's body was discovered in his London home.</code> |
+  | <code>e.&#9;in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> | <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  | <code>Helen Lederer is an English comedian .</code>                                                                                                                                                                                                                                                   | <code>Helen Lederer ( born 24 September 1954 ) is an English  : //www.scotsman.com/news/now-or-never-1-1396369 comedian , writer and actress who emerged as part of the alternative comedy boom at the beginning of the 1980s .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.025}
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,664 evaluation samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 29.01 tokens</li><li>max: 367 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 56.14 tokens</li><li>max: 389 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What planet did the voyager 1 spacecraft visit in 1980?</code>                                                                        | <code>The Voyager 1 spacecraft visited Saturn in 1980. Voyager 2 followed in 1981. These probes sent back detailed pictures of Saturn, its rings, and some of its moons ( Figure below ). From the Voyager data, we learned what Saturn’s rings are made of. They are particles of water and ice with a little bit of dust. There are several gaps in the rings. These gaps were cleared out by moons within the rings. Gravity attracts dust and gas to the moon from the ring. This leaves a gap in the rings. Other gaps in the rings are caused by the competing forces of Saturn and its moons outside the rings.</code> |
+  | <code>Diffusion Diffusion is a process where atoms or molecules move from areas of high concentration to areas of low concentration.</code> | <code>Diffusion is the process in which a substance naturally moves from an area of higher to lower concentration.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  | <code>Who had an 80s No 1 with Don't You Want Me?</code>                                                                                    | <code>The Human League - Don't You Want Me - YouTube The Human League - Don't You Want Me Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Feb 27, 2009 Music video by The Human League performing Don't You Want Me (2003 Digital Remaster). Category</code>                                                                                                                                     |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.025}
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 256
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
+- `warmup_ratio`: 0.33
+- `save_safetensors`: False
+- `fp16`: True
+- `push_to_hub`: True
+- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest3-step1-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 256
+- `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`: 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
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
+- `warmup_ratio`: 0.33
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `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`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `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
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest3-step1-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss | Validation Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap |
+|:------:|:----:|:-------------:|:---------------:|:------------------------:|:-----------------:|:---------------:|
+| 0.0010 | 1    | 10.4072       | -               | -                        | -                 | -               |
+| 0.0020 | 2    | 11.0865       | -               | -                        | -                 | -               |
+| 0.0030 | 3    | 9.5114        | -               | -                        | -                 | -               |
+| 0.0039 | 4    | 9.9584        | -               | -                        | -                 | -               |
+| 0.0049 | 5    | 10.068        | -               | -                        | -                 | -               |
+| 0.0059 | 6    | 11.0224       | -               | -                        | -                 | -               |
+| 0.0069 | 7    | 9.7703        | -               | -                        | -                 | -               |
+| 0.0079 | 8    | 10.5005       | -               | -                        | -                 | -               |
+| 0.0089 | 9    | 10.1987       | -               | -                        | -                 | -               |
+| 0.0098 | 10   | 10.0277       | -               | -                        | -                 | -               |
+| 0.0108 | 11   | 10.6965       | -               | -                        | -                 | -               |
+| 0.0118 | 12   | 10.0609       | -               | -                        | -                 | -               |
+| 0.0128 | 13   | 11.6214       | -               | -                        | -                 | -               |
+| 0.0138 | 14   | 9.4053        | -               | -                        | -                 | -               |
+| 0.0148 | 15   | 10.4014       | -               | -                        | -                 | -               |
+| 0.0157 | 16   | 10.4119       | -               | -                        | -                 | -               |
+| 0.0167 | 17   | 9.4658        | -               | -                        | -                 | -               |
+| 0.0177 | 18   | 9.2169        | -               | -                        | -                 | -               |
+| 0.0187 | 19   | 11.2337       | -               | -                        | -                 | -               |
+| 0.0197 | 20   | 11.0572       | -               | -                        | -                 | -               |
+| 0.0207 | 21   | 11.0452       | -               | -                        | -                 | -               |
+| 0.0217 | 22   | 10.31         | -               | -                        | -                 | -               |
+| 0.0226 | 23   | 9.1395        | -               | -                        | -                 | -               |
+| 0.0236 | 24   | 8.4201        | -               | -                        | -                 | -               |
+| 0.0246 | 25   | 8.6036        | -               | -                        | -                 | -               |
+| 0.0256 | 26   | 11.7579       | -               | -                        | -                 | -               |
+| 0.0266 | 27   | 10.1307       | -               | -                        | -                 | -               |
+| 0.0276 | 28   | 9.2915        | -               | -                        | -                 | -               |
+| 0.0285 | 29   | 9.0208        | -               | -                        | -                 | -               |
+| 0.0295 | 30   | 8.6867        | -               | -                        | -                 | -               |
+| 0.0305 | 31   | 8.0925        | -               | -                        | -                 | -               |
+| 0.0315 | 32   | 8.6617        | -               | -                        | -                 | -               |
+| 0.0325 | 33   | 8.3374        | -               | -                        | -                 | -               |
+| 0.0335 | 34   | 7.8566        | -               | -                        | -                 | -               |
+| 0.0344 | 35   | 9.0698        | -               | -                        | -                 | -               |
+| 0.0354 | 36   | 7.7727        | -               | -                        | -                 | -               |
+| 0.0364 | 37   | 7.6128        | -               | -                        | -                 | -               |
+| 0.0374 | 38   | 7.8762        | -               | -                        | -                 | -               |
+| 0.0384 | 39   | 7.5191        | -               | -                        | -                 | -               |
+| 0.0394 | 40   | 7.5638        | -               | -                        | -                 | -               |
+| 0.0404 | 41   | 7.1878        | -               | -                        | -                 | -               |
+| 0.0413 | 42   | 6.8878        | -               | -                        | -                 | -               |
+| 0.0423 | 43   | 7.5775        | -               | -                        | -                 | -               |
+| 0.0433 | 44   | 7.1076        | -               | -                        | -                 | -               |
+| 0.0443 | 45   | 6.5589        | -               | -                        | -                 | -               |
+| 0.0453 | 46   | 7.4456        | -               | -                        | -                 | -               |
+| 0.0463 | 47   | 6.8233        | -               | -                        | -                 | -               |
+| 0.0472 | 48   | 6.7633        | -               | -                        | -                 | -               |
+| 0.0482 | 49   | 6.6024        | -               | -                        | -                 | -               |
+| 0.0492 | 50   | 6.2778        | -               | -                        | -                 | -               |
+| 0.0502 | 51   | 6.1026        | -               | -                        | -                 | -               |
+| 0.0512 | 52   | 6.632         | -               | -                        | -                 | -               |
+| 0.0522 | 53   | 6.6962        | -               | -                        | -                 | -               |
+| 0.0531 | 54   | 5.8514        | -               | -                        | -                 | -               |
+| 0.0541 | 55   | 5.9951        | -               | -                        | -                 | -               |
+| 0.0551 | 56   | 5.4554        | -               | -                        | -                 | -               |
+| 0.0561 | 57   | 6.0147        | -               | -                        | -                 | -               |
+| 0.0571 | 58   | 5.215         | -               | -                        | -                 | -               |
+| 0.0581 | 59   | 6.4525        | -               | -                        | -                 | -               |
+| 0.0591 | 60   | 5.4048        | -               | -                        | -                 | -               |
+| 0.0600 | 61   | 5.0424        | -               | -                        | -                 | -               |
+| 0.0610 | 62   | 6.2646        | -               | -                        | -                 | -               |
+| 0.0620 | 63   | 5.0847        | -               | -                        | -                 | -               |
+| 0.0630 | 64   | 5.4415        | -               | -                        | -                 | -               |
+| 0.0640 | 65   | 5.2469        | -               | -                        | -                 | -               |
+| 0.0650 | 66   | 5.1378        | -               | -                        | -                 | -               |
+| 0.0659 | 67   | 5.1636        | -               | -                        | -                 | -               |
+| 0.0669 | 68   | 5.5596        | -               | -                        | -                 | -               |
+| 0.0679 | 69   | 4.9508        | -               | -                        | -                 | -               |
+| 0.0689 | 70   | 5.2355        | -               | -                        | -                 | -               |
+| 0.0699 | 71   | 4.7359        | -               | -                        | -                 | -               |
+| 0.0709 | 72   | 4.8947        | -               | -                        | -                 | -               |
+| 0.0719 | 73   | 4.6269        | -               | -                        | -                 | -               |
+| 0.0728 | 74   | 4.6072        | -               | -                        | -                 | -               |
+| 0.0738 | 75   | 4.9125        | -               | -                        | -                 | -               |
+| 0.0748 | 76   | 4.5856        | -               | -                        | -                 | -               |
+| 0.0758 | 77   | 4.7879        | -               | -                        | -                 | -               |
+| 0.0768 | 78   | 4.5348        | -               | -                        | -                 | -               |
+| 0.0778 | 79   | 4.3554        | -               | -                        | -                 | -               |
+| 0.0787 | 80   | 4.2984        | -               | -                        | -                 | -               |
+| 0.0797 | 81   | 4.5505        | -               | -                        | -                 | -               |
+| 0.0807 | 82   | 4.5325        | -               | -                        | -                 | -               |
+| 0.0817 | 83   | 4.2725        | -               | -                        | -                 | -               |
+| 0.0827 | 84   | 4.3054        | -               | -                        | -                 | -               |
+| 0.0837 | 85   | 4.5536        | -               | -                        | -                 | -               |
+| 0.0846 | 86   | 4.0265        | -               | -                        | -                 | -               |
+| 0.0856 | 87   | 4.7453        | -               | -                        | -                 | -               |
+| 0.0866 | 88   | 4.071         | -               | -                        | -                 | -               |
+| 0.0876 | 89   | 4.1582        | -               | -                        | -                 | -               |
+| 0.0886 | 90   | 4.1131        | -               | -                        | -                 | -               |
+| 0.0896 | 91   | 3.6582        | -               | -                        | -                 | -               |
+| 0.0906 | 92   | 4.143         | -               | -                        | -                 | -               |
+| 0.0915 | 93   | 4.2273        | -               | -                        | -                 | -               |
+| 0.0925 | 94   | 3.9321        | -               | -                        | -                 | -               |
+| 0.0935 | 95   | 4.2061        | -               | -                        | -                 | -               |
+| 0.0945 | 96   | 4.1042        | -               | -                        | -                 | -               |
+| 0.0955 | 97   | 3.9513        | -               | -                        | -                 | -               |
+| 0.0965 | 98   | 3.8627        | -               | -                        | -                 | -               |
+| 0.0974 | 99   | 4.3613        | -               | -                        | -                 | -               |
+| 0.0984 | 100  | 3.8513        | -               | -                        | -                 | -               |
+| 0.0994 | 101  | 3.5866        | -               | -                        | -                 | -               |
+| 0.1004 | 102  | 3.5239        | -               | -                        | -                 | -               |
+| 0.1014 | 103  | 3.5921        | -               | -                        | -                 | -               |
+| 0.1024 | 104  | 3.5962        | -               | -                        | -                 | -               |
+| 0.1033 | 105  | 4.0001        | -               | -                        | -                 | -               |
+| 0.1043 | 106  | 4.1374        | -               | -                        | -                 | -               |
+| 0.1053 | 107  | 3.9049        | -               | -                        | -                 | -               |
+| 0.1063 | 108  | 3.2511        | -               | -                        | -                 | -               |
+| 0.1073 | 109  | 3.2479        | -               | -                        | -                 | -               |
+| 0.1083 | 110  | 3.6414        | -               | -                        | -                 | -               |
+| 0.1093 | 111  | 3.6429        | -               | -                        | -                 | -               |
+| 0.1102 | 112  | 3.423         | -               | -                        | -                 | -               |
+| 0.1112 | 113  | 3.4967        | -               | -                        | -                 | -               |
+| 0.1122 | 114  | 3.7649        | -               | -                        | -                 | -               |
+| 0.1132 | 115  | 3.2845        | -               | -                        | -                 | -               |
+| 0.1142 | 116  | 3.356         | -               | -                        | -                 | -               |
+| 0.1152 | 117  | 3.2086        | -               | -                        | -                 | -               |
+| 0.1161 | 118  | 3.5561        | -               | -                        | -                 | -               |
+| 0.1171 | 119  | 3.7353        | -               | -                        | -                 | -               |
+| 0.1181 | 120  | 3.403         | -               | -                        | -                 | -               |
+| 0.1191 | 121  | 3.1009        | -               | -                        | -                 | -               |
+| 0.1201 | 122  | 3.2139        | -               | -                        | -                 | -               |
+| 0.1211 | 123  | 3.3339        | -               | -                        | -                 | -               |
+| 0.1220 | 124  | 2.9464        | -               | -                        | -                 | -               |
+| 0.1230 | 125  | 3.3366        | -               | -                        | -                 | -               |
+| 0.1240 | 126  | 3.0618        | -               | -                        | -                 | -               |
+| 0.125  | 127  | 3.0092        | -               | -                        | -                 | -               |
+| 0.1260 | 128  | 2.7152        | -               | -                        | -                 | -               |
+| 0.1270 | 129  | 2.9423        | -               | -                        | -                 | -               |
+| 0.1280 | 130  | 2.6569        | -               | -                        | -                 | -               |
+| 0.1289 | 131  | 2.8469        | -               | -                        | -                 | -               |
+| 0.1299 | 132  | 2.9089        | -               | -                        | -                 | -               |
+| 0.1309 | 133  | 2.5809        | -               | -                        | -                 | -               |
+| 0.1319 | 134  | 2.6987        | -               | -                        | -                 | -               |
+| 0.1329 | 135  | 3.2518        | -               | -                        | -                 | -               |
+| 0.1339 | 136  | 2.9145        | -               | -                        | -                 | -               |
+| 0.1348 | 137  | 2.4809        | -               | -                        | -                 | -               |
+| 0.1358 | 138  | 2.8264        | -               | -                        | -                 | -               |
+| 0.1368 | 139  | 2.5724        | -               | -                        | -                 | -               |
+| 0.1378 | 140  | 2.6949        | -               | -                        | -                 | -               |
+| 0.1388 | 141  | 2.6925        | -               | -                        | -                 | -               |
+| 0.1398 | 142  | 2.9311        | -               | -                        | -                 | -               |
+| 0.1407 | 143  | 2.5667        | -               | -                        | -                 | -               |
+| 0.1417 | 144  | 3.2471        | -               | -                        | -                 | -               |
+| 0.1427 | 145  | 2.2441        | -               | -                        | -                 | -               |
+| 0.1437 | 146  | 2.75          | -               | -                        | -                 | -               |
+| 0.1447 | 147  | 2.9669        | -               | -                        | -                 | -               |
+| 0.1457 | 148  | 2.736         | -               | -                        | -                 | -               |
+| 0.1467 | 149  | 3.104         | -               | -                        | -                 | -               |
+| 0.1476 | 150  | 2.2175        | -               | -                        | -                 | -               |
+| 0.1486 | 151  | 2.7415        | -               | -                        | -                 | -               |
+| 0.1496 | 152  | 1.8707        | -               | -                        | -                 | -               |
+| 0.1506 | 153  | 2.5961        | 2.2653          | 0.3116                   | 0.4265            | 0.6462          |
+| 0.1516 | 154  | 3.1149        | -               | -                        | -                 | -               |
+| 0.1526 | 155  | 2.2976        | -               | -                        | -                 | -               |
+| 0.1535 | 156  | 2.4436        | -               | -                        | -                 | -               |
+| 0.1545 | 157  | 2.8826        | -               | -                        | -                 | -               |
+| 0.1555 | 158  | 2.3664        | -               | -                        | -                 | -               |
+| 0.1565 | 159  | 2.2485        | -               | -                        | -                 | -               |
+| 0.1575 | 160  | 2.5167        | -               | -                        | -                 | -               |
+| 0.1585 | 161  | 1.7183        | -               | -                        | -                 | -               |
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+| 0.2943 | 299  | 0.974         | -               | -                        | -                 | -               |
+| 0.2953 | 300  | 1.53          | -               | -                        | -                 | -               |
+| 0.2963 | 301  | 1.5985        | -               | -                        | -                 | -               |
+| 0.2972 | 302  | 1.2169        | -               | -                        | -                 | -               |
+| 0.2982 | 303  | 1.771         | -               | -                        | -                 | -               |
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+| 0.3002 | 305  | 1.9496        | -               | -                        | -                 | -               |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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