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 sha256:5f1a56dcfcfbff23630b549e575f6ec58439394ba18910f67aaa7762af6f7270
+size 18940723

checkpoint-305/README.md

@@ -0,0 +1,1174 @@
+---
+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: What was the name of Jed's nephew in The Beverly Hillbillies?
+  sentences:
+  - Jed Clampett - The Beverly Hillbillies Characters - ShareTV Buddy Ebsen began
+    his career as a dancer in the late 1920s in a Broadway chorus. He later formed
+    a vaudeville ... Character Bio Although he had received little formal education,
+    Jed Clampett had a good deal of common sense. A good-natured man, he is the apparent
+    head of the family. Jed's wife (Elly May's mother) died, but is referred to in
+    the episode "Duke Steals A Wife" as Rose Ellen. Jed was shown to be an expert
+    marksman and was extremely loyal to his family and kinfolk. The huge oil pool
+    in the swamp he owned was the beginning of his rags-to-riches journey to Beverly
+    Hills. Although he longed for the old ways back in the hills, he made the best
+    of being in Beverly Hills. Whenever he had anything on his mind, he would sit
+    on the curbstone of his mansion and whittle until he came up with the answer.
+    Jedediah, the version of Jed's name used in the 1993 Beverly Hillbillies theatrical
+    movie, was never mentioned in the original television series (though coincidentally,
+    on Ebsen's subsequent series, Barnaby Jones, Barnaby's nephew J.R. was also named
+    Jedediah). In one episode Jed and Granny reminisce about seeing Buddy Ebsen and
+    Vilma Ebsen—a joking reference to the Ebsens' song and dance act. Jed appears
+    in all 274 episodes. Episode Screenshots
+  - a stove generates heat for cooking usually
+  - Miss Marple series by Agatha Christie Miss Marple series 43 works, 13 primary
+    works Mystery series in order of publication. Miss Marple is introduced in The
+    Murder at the Vicarage but the books can be read in any order. Mixed short story
+    collections are included if some are Marple, often have horror, supernatural,
+    maybe detective Poirot, Pyne, or Quin. Note that "Nemesis" should be read AFTER
+    "A Caribbean Holiday"
+- source_sentence: A recording of folk songs done for the Columbia society in 1942
+    was largely arranged by Pjetër Dungu .
+  sentences:
+  - Someone cooking drugs in a spoon over a candle
+  - A recording of folk songs made for the Columbia society in 1942 was largely arranged
+    by Pjetër Dungu .
+  - A Murder of Crows, A Parliament of Owls What do You Call a Group of Birds? Do
+    you know what a group of Ravens is called? What about a group of peacocks, snipe
+    or hummingbirds? Here is a list of Bird Collectives, terms that you can use to
+    describe a    group of birds. Birds in general
+- source_sentence: A person in a kitchen looking at the oven.
+  sentences:
+  - "staying warm has a positive impact on an animal 's survival. Furry animals grow\
+    \ thicker coats to keep warm in the winter. \n Furry animals grow thicker coats\
+    \ which has a positive impact on their survival. "
+  - A woman In the kitchen opening her oven.
+  - EE has apologised after a fault left some of its customers unable to use the internet
+    on their mobile devices.
+- source_sentence: Air can be separated into several elements.
+  sentences:
+  - Which of the following substances can be separated into several elements?
+  - 'Funny Interesting Facts Humor Strange: Carl and the Passions changed band name
+    to what Carl and the Passions changed band name to what Beach Boys Carl and the
+    Passions - "So Tough" is the fifteenth studio album released by The Beach Boys
+    in 1972. In its initial release, it was the second disc of a two-album set with
+    Pet Sounds (which The Beach Boys were able to license from Capitol Records). Unfortunately,
+    due to the fact that Carl and the Passions - "So Tough" was a transitional album
+    that saw the departure of one member and the introduction of two new ones, making
+    it wildly inconsistent in terms of type of material present, it paled next to
+    their 1966 classic and was seen as something of a disappointment in its time of
+    release. The title of the album itself was a reference to an early band Carl Wilson
+    had been in as a teenager (some say a possible early name for the Beach Boys).
+    It was also the first album released under a new deal with Warner Bros. that allowed
+    the company to distribute all future Beach Boys product in foreign as well as
+    domestic markets.'
+  - Which statement correctly describes a relationship between two human body systems?
+- source_sentence: What do outdoor plants require to survive?
+  sentences:
+  - "a plants require water for survival. If no rain or watering, the plant dies.\
+    \ \n Outdoor plants require rain to survive."
+  - (Vegan) soups are nutritious. In addition to them being easy to digest, most the
+    time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables,
+    and beans. Because the soup is full of those nutrients AND that it's easy to digest,
+    your body is able to absorb more of those nutrients into your system.
+  - If you do the math, there are 11,238,513 possible combinations of five white balls
+    (without order mattering). Multiply that by the 26 possible red balls, and you
+    get 292,201,338 possible Powerball number combinations. At $2 per ticket, you'd
+    need $584,402,676 to buy every single combination and guarantee a win.
+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: 0.12009124140478655
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.180573622028628
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.18492770691981375
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.21139381574888486
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.15529980522625675
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.18058248277838349
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.11997652374043644
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.18041242798509616
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.18492770691981375
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.21139381574888486
+      name: Spearman Max
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: allNLI dev
+      type: allNLI-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.66796875
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9721524119377136
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.5029239766081871
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.821484386920929
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.33659491193737767
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9942196531791907
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.3857994503224615
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.66796875
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 746.914794921875
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.5029239766081871
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 631.138916015625
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.33659491193737767
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9942196531791907
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.38572844452312516
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.666015625
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 95.24527740478516
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.5045317220543807
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 254.973388671875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.34151329243353784
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.9653179190751445
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.39193409293721965
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.66796875
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 6.541449546813965
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.5029239766081871
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 16.558998107910156
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.33659491193737767
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.9942196531791907
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.3858031188548441
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.66796875
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 746.914794921875
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.5045317220543807
+      name: Max F1
+    - type: max_f1_threshold
+      value: 631.138916015625
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.34151329243353784
+      name: Max Precision
+    - type: max_recall
+      value: 0.9942196531791907
+      name: Max Recall
+    - type: max_ap
+      value: 0.39193409293721965
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Qnli dev
+      type: Qnli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.58203125
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9368094801902771
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.6300268096514745
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.802739143371582
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.46078431372549017
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9957627118644068
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.5484497034083067
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.58203125
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 719.7518310546875
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.6300268096514745
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 616.7227783203125
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.46078431372549017
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.9957627118644068
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.548461685358088
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.607421875
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 182.1275177001953
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.6303724928366763
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 230.0565185546875
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.47619047619047616
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.9322033898305084
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.5750034744442096
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.58203125
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 9.853867530822754
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.6300268096514745
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 17.40953254699707
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.46078431372549017
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.9957627118644068
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.5484497034083067
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.607421875
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 719.7518310546875
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.6303724928366763
+      name: Max F1
+    - type: max_f1_threshold
+      value: 616.7227783203125
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.47619047619047616
+      name: Max Precision
+    - type: max_recall
+      value: 0.9957627118644068
+      name: Max Recall
+    - type: max_ap
+      value: 0.5750034744442096
+      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-toytest2-step1-checkpoints-tmp")
+# Run inference
+sentences = [
+    'What do outdoor plants require to survive?',
+    'a plants require water for survival. If no rain or watering, the plant dies. \n Outdoor plants require rain to survive.',
+    "(Vegan) soups are nutritious. In addition to them being easy to digest, most the time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables, and beans. Because the soup is full of those nutrients AND that it's easy to digest, your body is able to absorb more of those nutrients into your system.",
+]
+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      | 0.1201     |
+| **spearman_cosine** | **0.1806** |
+| pearson_manhattan   | 0.1849     |
+| spearman_manhattan  | 0.2114     |
+| pearson_euclidean   | 0.1553     |
+| spearman_euclidean  | 0.1806     |
+| pearson_dot         | 0.12       |
+| spearman_dot        | 0.1804     |
+| pearson_max         | 0.1849     |
+| spearman_max        | 0.2114     |
+
+#### 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              | 0.668      |
+| cosine_accuracy_threshold    | 0.9722     |
+| cosine_f1                    | 0.5029     |
+| cosine_f1_threshold          | 0.8215     |
+| cosine_precision             | 0.3366     |
+| cosine_recall                | 0.9942     |
+| cosine_ap                    | 0.3858     |
+| dot_accuracy                 | 0.668      |
+| dot_accuracy_threshold       | 746.9148   |
+| dot_f1                       | 0.5029     |
+| dot_f1_threshold             | 631.1389   |
+| dot_precision                | 0.3366     |
+| dot_recall                   | 0.9942     |
+| dot_ap                       | 0.3857     |
+| manhattan_accuracy           | 0.666      |
+| manhattan_accuracy_threshold | 95.2453    |
+| manhattan_f1                 | 0.5045     |
+| manhattan_f1_threshold       | 254.9734   |
+| manhattan_precision          | 0.3415     |
+| manhattan_recall             | 0.9653     |
+| manhattan_ap                 | 0.3919     |
+| euclidean_accuracy           | 0.668      |
+| euclidean_accuracy_threshold | 6.5414     |
+| euclidean_f1                 | 0.5029     |
+| euclidean_f1_threshold       | 16.559     |
+| euclidean_precision          | 0.3366     |
+| euclidean_recall             | 0.9942     |
+| euclidean_ap                 | 0.3858     |
+| max_accuracy                 | 0.668      |
+| max_accuracy_threshold       | 746.9148   |
+| max_f1                       | 0.5045     |
+| max_f1_threshold             | 631.1389   |
+| max_precision                | 0.3415     |
+| max_recall                   | 0.9942     |
+| **max_ap**                   | **0.3919** |
+
+#### 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              | 0.582     |
+| cosine_accuracy_threshold    | 0.9368    |
+| cosine_f1                    | 0.63      |
+| cosine_f1_threshold          | 0.8027    |
+| cosine_precision             | 0.4608    |
+| cosine_recall                | 0.9958    |
+| cosine_ap                    | 0.5484    |
+| dot_accuracy                 | 0.582     |
+| dot_accuracy_threshold       | 719.7518  |
+| dot_f1                       | 0.63      |
+| dot_f1_threshold             | 616.7228  |
+| dot_precision                | 0.4608    |
+| dot_recall                   | 0.9958    |
+| dot_ap                       | 0.5485    |
+| manhattan_accuracy           | 0.6074    |
+| manhattan_accuracy_threshold | 182.1275  |
+| manhattan_f1                 | 0.6304    |
+| manhattan_f1_threshold       | 230.0565  |
+| manhattan_precision          | 0.4762    |
+| manhattan_recall             | 0.9322    |
+| manhattan_ap                 | 0.575     |
+| euclidean_accuracy           | 0.582     |
+| euclidean_accuracy_threshold | 9.8539    |
+| euclidean_f1                 | 0.63      |
+| euclidean_f1_threshold       | 17.4095   |
+| euclidean_precision          | 0.4608    |
+| euclidean_recall             | 0.9958    |
+| euclidean_ap                 | 0.5484    |
+| max_accuracy                 | 0.6074    |
+| max_accuracy_threshold       | 719.7518  |
+| max_f1                       | 0.6304    |
+| max_f1_threshold             | 616.7228  |
+| max_precision                | 0.4762    |
+| max_recall                   | 0.9958    |
+| **max_ap**                   | **0.575** |
+
+<!--
+## 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: 29.43 tokens</li><li>max: 400 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.02 tokens</li><li>max: 389 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                             | sentence2                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the chemical symbol for Silver?</code>                                                                                                                                                                                                                                                  | <code>Chemical Elements.com - Silver (Ag) Bentor, Yinon. Chemical Element.com - Silver. <http://www.chemicalelements.com/elements/ag.html>. For more information about citing online sources, please visit the MLA's Website . This page was created by Yinon Bentor. Use of this web site is restricted by this site's license  agreement . Copyright © 1996-2012 Yinon Bentor. All Rights Reserved.</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>Keanu Neal was born in 1995 .</code>                                                                                                                                                                                                                                                            | <code>Keanu Neal ( born July 26 , 1995 ) is an American football safety for the Atlanta Falcons of the National Football League ( NFL ) .</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: 28.9 tokens</li><li>max: 348 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 57.31 tokens</li><li>max: 450 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Gene expression is regulated primarily at the what level?</code>                                                                      | <code>Gene expression is regulated primarily at the transcriptional level.</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>In which James Bond film did Sean Connery wear the Bell Rocket Belt (Jet Pack)?</code>                                                | <code>Jet Pack - James Bond Gadgets 125lbs Summary James Bond used the Jetpack in 1965's Thunderball, to escape from gunmen after killing a SPECTRE agent. The Jetpack In the 1965 movie Thunderball, James Bond (Sean Connery) uses Q's Jetpack to escape from two gunmen after killing Jacques Bouvar, SPECTRE Agent No. 6. It was also used in the Thunderball movie posters, being the "Look Up" part of the "Look Up! Look Down! Look Out!" tagline. The Jetpack returned in the 2002 movie Die Another Day, in the Q scene that showcased many other classic gadgets. The Jetpack is a very popular Bond gadget and is a favorite among many fans due to its originality and uniqueness. The Bell Rocket Belt The Jetpack is actually a Bell Rocket Belt, a fully functional rocket pack device. It was designed for use in the army, but was rejected because of its short flying time of 21-22 seconds. Powered by hydrogen peroxide, it could fly about 250m and reach a maximum altitude of 18m, going 55km/h. Despite its impracticality in the real world, the Jetpack made a spectacular debut in Thunderball. Although Sean Connery is seen in the takeoff and landings, the main flight was piloted by Gordon Yeager and Bill Suitor.</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-toytest2-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-toytest2-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    | 18.7427       | -               | -                        | -                 | -               |
+| 0.0020 | 2    | 11.6434       | -               | -                        | -                 | -               |
+| 0.0030 | 3    | 7.4859        | -               | -                        | -                 | -               |
+| 0.0039 | 4    | 7.3779        | -               | -                        | -                 | -               |
+| 0.0049 | 5    | 17.5878       | -               | -                        | -                 | -               |
+| 0.0059 | 6    | 8.4984        | -               | -                        | -                 | -               |
+| 0.0069 | 7    | 8.375         | -               | -                        | -                 | -               |
+| 0.0079 | 8    | 7.3241        | -               | -                        | -                 | -               |
+| 0.0089 | 9    | 10.3081       | -               | -                        | -                 | -               |
+| 0.0098 | 10   | 8.5363        | -               | -                        | -                 | -               |
+| 0.0108 | 11   | 17.2241       | -               | -                        | -                 | -               |
+| 0.0118 | 12   | 7.575         | -               | -                        | -                 | -               |
+| 0.0128 | 13   | 9.1905        | -               | -                        | -                 | -               |
+| 0.0138 | 14   | 11.7727       | -               | -                        | -                 | -               |
+| 0.0148 | 15   | 9.5827        | -               | -                        | -                 | -               |
+| 0.0157 | 16   | 7.4432        | -               | -                        | -                 | -               |
+| 0.0167 | 17   | 7.1573        | -               | -                        | -                 | -               |
+| 0.0177 | 18   | 19.8016       | -               | -                        | -                 | -               |
+| 0.0187 | 19   | 19.5118       | -               | -                        | -                 | -               |
+| 0.0197 | 20   | 7.9062        | -               | -                        | -                 | -               |
+| 0.0207 | 21   | 8.6791        | -               | -                        | -                 | -               |
+| 0.0217 | 22   | 7.7318        | -               | -                        | -                 | -               |
+| 0.0226 | 23   | 7.9319        | -               | -                        | -                 | -               |
+| 0.0236 | 24   | 7.192         | -               | -                        | -                 | -               |
+| 0.0246 | 25   | 15.5799       | -               | -                        | -                 | -               |
+| 0.0256 | 26   | 9.7859        | -               | -                        | -                 | -               |
+| 0.0266 | 27   | 9.9259        | -               | -                        | -                 | -               |
+| 0.0276 | 28   | 6.3076        | -               | -                        | -                 | -               |
+| 0.0285 | 29   | 7.4471        | -               | -                        | -                 | -               |
+| 0.0295 | 30   | 7.1246        | -               | -                        | -                 | -               |
+| 0.0305 | 31   | 6.5505        | -               | -                        | -                 | -               |
+| 0.0315 | 32   | 18.5194       | -               | -                        | -                 | -               |
+| 0.0325 | 33   | 7.0747        | -               | -                        | -                 | -               |
+| 0.0335 | 34   | 14.9456       | -               | -                        | -                 | -               |
+| 0.0344 | 35   | 6.608         | -               | -                        | -                 | -               |
+| 0.0354 | 36   | 8.4672        | -               | -                        | -                 | -               |
+| 0.0364 | 37   | 6.8853        | -               | -                        | -                 | -               |
+| 0.0374 | 38   | 13.6063       | -               | -                        | -                 | -               |
+| 0.0384 | 39   | 7.2625        | -               | -                        | -                 | -               |
+| 0.0394 | 40   | 6.2234        | -               | -                        | -                 | -               |
+| 0.0404 | 41   | 14.9675       | -               | -                        | -                 | -               |
+| 0.0413 | 42   | 6.6038        | -               | -                        | -                 | -               |
+| 0.0423 | 43   | 13.1173       | -               | -                        | -                 | -               |
+| 0.0433 | 44   | 16.6992       | -               | -                        | -                 | -               |
+| 0.0443 | 45   | 6.4828        | -               | -                        | -                 | -               |
+| 0.0453 | 46   | 5.9815        | -               | -                        | -                 | -               |
+| 0.0463 | 47   | 6.1738        | -               | -                        | -                 | -               |
+| 0.0472 | 48   | 7.134         | -               | -                        | -                 | -               |
+| 0.0482 | 49   | 9.3933        | -               | -                        | -                 | -               |
+| 0.0492 | 50   | 10.8085       | -               | -                        | -                 | -               |
+| 0.0502 | 51   | 11.4172       | -               | -                        | -                 | -               |
+| 0.0512 | 52   | 7.3397        | -               | -                        | -                 | -               |
+| 0.0522 | 53   | 5.8851        | -               | -                        | -                 | -               |
+| 0.0531 | 54   | 6.8105        | -               | -                        | -                 | -               |
+| 0.0541 | 55   | 5.3637        | -               | -                        | -                 | -               |
+| 0.0551 | 56   | 6.2628        | -               | -                        | -                 | -               |
+| 0.0561 | 57   | 6.0039        | -               | -                        | -                 | -               |
+| 0.0571 | 58   | 7.5859        | -               | -                        | -                 | -               |
+| 0.0581 | 59   | 6.0802        | -               | -                        | -                 | -               |
+| 0.0591 | 60   | 5.5822        | -               | -                        | -                 | -               |
+| 0.0600 | 61   | 5.8773        | -               | -                        | -                 | -               |
+| 0.0610 | 62   | 6.0814        | -               | -                        | -                 | -               |
+| 0.0620 | 63   | 5.4483        | -               | -                        | -                 | -               |
+| 0.0630 | 64   | 10.2506       | -               | -                        | -                 | -               |
+| 0.0640 | 65   | 10.5976       | -               | -                        | -                 | -               |
+| 0.0650 | 66   | 6.9942        | -               | -                        | -                 | -               |
+| 0.0659 | 67   | 5.4813        | -               | -                        | -                 | -               |
+| 0.0669 | 68   | 7.045         | -               | -                        | -                 | -               |
+| 0.0679 | 69   | 5.8549        | -               | -                        | -                 | -               |
+| 0.0689 | 70   | 8.8514        | -               | -                        | -                 | -               |
+| 0.0699 | 71   | 5.2557        | -               | -                        | -                 | -               |
+| 0.0709 | 72   | 5.1181        | -               | -                        | -                 | -               |
+| 0.0719 | 73   | 5.5331        | -               | -                        | -                 | -               |
+| 0.0728 | 74   | 5.5944        | -               | -                        | -                 | -               |
+| 0.0738 | 75   | 4.6332        | -               | -                        | -                 | -               |
+| 0.0748 | 76   | 4.9532        | -               | -                        | -                 | -               |
+| 0.0758 | 77   | 5.055         | -               | -                        | -                 | -               |
+| 0.0768 | 78   | 4.5005        | -               | -                        | -                 | -               |
+| 0.0778 | 79   | 5.1997        | -               | -                        | -                 | -               |
+| 0.0787 | 80   | 5.1479        | -               | -                        | -                 | -               |
+| 0.0797 | 81   | 5.1777        | -               | -                        | -                 | -               |
+| 0.0807 | 82   | 5.5565        | -               | -                        | -                 | -               |
+| 0.0817 | 83   | 4.6999        | -               | -                        | -                 | -               |
+| 0.0827 | 84   | 5.0681        | -               | -                        | -                 | -               |
+| 0.0837 | 85   | 5.2208        | -               | -                        | -                 | -               |
+| 0.0846 | 86   | 4.56          | -               | -                        | -                 | -               |
+| 0.0856 | 87   | 4.6793        | -               | -                        | -                 | -               |
+| 0.0866 | 88   | 4.4611        | -               | -                        | -                 | -               |
+| 0.0876 | 89   | 9.623         | -               | -                        | -                 | -               |
+| 0.0886 | 90   | 5.0316        | -               | -                        | -                 | -               |
+| 0.0896 | 91   | 4.1771        | -               | -                        | -                 | -               |
+| 0.0906 | 92   | 4.9652        | -               | -                        | -                 | -               |
+| 0.0915 | 93   | 8.7432        | -               | -                        | -                 | -               |
+| 0.0925 | 94   | 4.6234        | -               | -                        | -                 | -               |
+| 0.0935 | 95   | 4.4016        | -               | -                        | -                 | -               |
+| 0.0945 | 96   | 4.9903        | -               | -                        | -                 | -               |
+| 0.0955 | 97   | 4.5606        | -               | -                        | -                 | -               |
+| 0.0965 | 98   | 4.9534        | -               | -                        | -                 | -               |
+| 0.0974 | 99   | 8.1838        | -               | -                        | -                 | -               |
+| 0.0984 | 100  | 4.9736        | -               | -                        | -                 | -               |
+| 0.0994 | 101  | 4.4733        | -               | -                        | -                 | -               |
+| 0.1004 | 102  | 4.9725        | -               | -                        | -                 | -               |
+| 0.1014 | 103  | 4.5861        | -               | -                        | -                 | -               |
+| 0.1024 | 104  | 7.7634        | -               | -                        | -                 | -               |
+| 0.1033 | 105  | 4.9915        | -               | -                        | -                 | -               |
+| 0.1043 | 106  | 5.1391        | -               | -                        | -                 | -               |
+| 0.1053 | 107  | 5.0157        | -               | -                        | -                 | -               |
+| 0.1063 | 108  | 4.0982        | -               | -                        | -                 | -               |
+| 0.1073 | 109  | 4.2178        | -               | -                        | -                 | -               |
+| 0.1083 | 110  | 4.6193        | -               | -                        | -                 | -               |
+| 0.1093 | 111  | 4.7638        | -               | -                        | -                 | -               |
+| 0.1102 | 112  | 4.1207        | -               | -                        | -                 | -               |
+| 0.1112 | 113  | 5.2034        | -               | -                        | -                 | -               |
+| 0.1122 | 114  | 5.0693        | -               | -                        | -                 | -               |
+| 0.1132 | 115  | 4.7895        | -               | -                        | -                 | -               |
+| 0.1142 | 116  | 4.9486        | -               | -                        | -                 | -               |
+| 0.1152 | 117  | 4.6552        | -               | -                        | -                 | -               |
+| 0.1161 | 118  | 4.4555        | -               | -                        | -                 | -               |
+| 0.1171 | 119  | 4.8977        | -               | -                        | -                 | -               |
+| 0.1181 | 120  | 7.6836        | -               | -                        | -                 | -               |
+| 0.1191 | 121  | 4.8106        | -               | -                        | -                 | -               |
+| 0.1201 | 122  | 4.9958        | -               | -                        | -                 | -               |
+| 0.1211 | 123  | 4.4585        | -               | -                        | -                 | -               |
+| 0.1220 | 124  | 7.5559        | -               | -                        | -                 | -               |
+| 0.1230 | 125  | 4.2636        | -               | -                        | -                 | -               |
+| 0.1240 | 126  | 4.0436        | -               | -                        | -                 | -               |
+| 0.125  | 127  | 4.7416        | -               | -                        | -                 | -               |
+| 0.1260 | 128  | 4.2215        | -               | -                        | -                 | -               |
+| 0.1270 | 129  | 6.3561        | -               | -                        | -                 | -               |
+| 0.1280 | 130  | 6.2299        | -               | -                        | -                 | -               |
+| 0.1289 | 131  | 4.3492        | -               | -                        | -                 | -               |
+| 0.1299 | 132  | 4.0216        | -               | -                        | -                 | -               |
+| 0.1309 | 133  | 6.963         | -               | -                        | -                 | -               |
+| 0.1319 | 134  | 3.9474        | -               | -                        | -                 | -               |
+| 0.1329 | 135  | 4.3437        | -               | -                        | -                 | -               |
+| 0.1339 | 136  | 3.6267        | -               | -                        | -                 | -               |
+| 0.1348 | 137  | 3.9896        | -               | -                        | -                 | -               |
+| 0.1358 | 138  | 4.8156        | -               | -                        | -                 | -               |
+| 0.1368 | 139  | 4.9751        | -               | -                        | -                 | -               |
+| 0.1378 | 140  | 4.4144        | -               | -                        | -                 | -               |
+| 0.1388 | 141  | 4.7213        | -               | -                        | -                 | -               |
+| 0.1398 | 142  | 6.6081        | -               | -                        | -                 | -               |
+| 0.1407 | 143  | 4.2929        | -               | -                        | -                 | -               |
+| 0.1417 | 144  | 4.2537        | -               | -                        | -                 | -               |
+| 0.1427 | 145  | 4.0647        | -               | -                        | -                 | -               |
+| 0.1437 | 146  | 3.937         | -               | -                        | -                 | -               |
+| 0.1447 | 147  | 5.6582        | -               | -                        | -                 | -               |
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+| 0.2953 | 300  | 3.3505        | -               | -                        | -                 | -               |
+| 0.2963 | 301  | 2.9615        | -               | -                        | -                 | -               |
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+| 0.3002 | 305  | 2.7168        | -               | -                        | -                 | -               |
+
+</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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