metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2048
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-distilroberta-v1
widget:
  - source_sentence: Can you provide the link to the Discrete Math final exam from 2024?
    sentences:
      - >-
        The final exam for Discrete Math course, offered by the general
        department, from 2024, is available at the following link:
        [https://drive.google.com/file/d/1pCpnVt6IiOTMlGTYw3sUZ8NEnI3thwO5/view?usp=sharing
      - >-
        The final exam for internet of things course, offered by the computer
        science department, from 2025, is available at the following link:
        [https://drive.google.com/file/d/1UjtShx1hFNg8_gB5NsqGDGKAvpkkBfm9/view?usp=sharing
      - >-
        The final exam for the physics1 course, offered by the general
        department, from 2018, is available at the following link:
        [https://drive.google.com/file/d/1T-KLo2JW3fLFSu1hT7WtGOnmXFQTqMin/view].
  - source_sentence: Can you provide the exam link for the Physics 1 course from 2023?
    sentences:
      - >-
        The final exam for the physics1 course, offered by the general
        department, from 2023, is available at the following link:
        [https://drive.google.com/file/d/1TrlV8yBdNHJjGVsDBD6EU2A4G80nU1kV/view?usp=sharing].
      - >-
        The final exam for the Probability & Statistics course, offered by the
        general department, from 2021, is available at the following link:
        [https://drive.google.com/drive/u/2/folders/1c2w87tPBcFazujOmQ1ZKmiuR__EIsQd3].
      - >-
        Dr. Noran el sayed is part of the Unknown department and can be reached
        at [email protected].
  - source_sentence: >-
      How can I access the final exam for the Software Engineering class from
      2015?
    sentences:
      - >-
        The final exam for Software Engineering course, offered by the
        information system department, from 2015, is available at the following
        link:
        [https://drive.google.com/file/d/1ve8sh5HhCeQqr_swbADxYiYvJRkFBiAi/view
      - >-
        Dr. Ahmed Soliman (Ahmed Nagiub) is part of the Unknown department and
        can be reached at [email protected].
      - >-
        The final exam for Software Engineering course, offered by the
        information system department, from 2020, is available at the following
        link:
        [https://drive.google.com/file/d/1qYvsJGm5FWTq9L7TlJOGg85vPHtu7G6d/view
  - source_sentence: Is there a link available for the 2023 Probability & Stats course exam?
    sentences:
      - >-
        The final exam for operating system course, offered by the computer
        science department, from 2024, is available at the following link:
        [https://drive.google.com/file/d/1ITc9Hs3s0sw8SPEfKSAlE-sQTngL5oaL/view?usp=sharing
      - >-
        The final exam for the Probability & Statistics course, offered by the
        general department, from 2023, is available at the following link:
        [https://drive.google.com/file/d/1kh3KbahqTnCSNwqDyB8iSPSIMQ9B9ZUZ/view?usp=sharing].
      - >-
        The final exam for computer Architecture and organization course,
        offered by the general department, from 2024, is available at the
        following link:
        [https://drive.google.com/file/d/1BBVB6U8nnEA8sLUlmR3J52TD8kjWlGWM/view?usp=sharing
  - source_sentence: >-
      How do I access the final exam for the Digital Image Processing course
      from 2016?
    sentences:
      - >-
        The final exam for the Statistical Analysis course, offered by the
        general department, from 2025, is available at the following link:
        [https://drive.google.com/file/d/14Fi9uMdy0JRw7Wp2j1-2eNoRd5CwS_ng/view?usp=sharing
      - >-
        The final exam for Digital Image Processing course, offered by the
        computer science department, from 2016, is available at the following
        link:
        [https://drive.google.com/file/d/1dUDU-VM5_c7Wst98iTC83GhudfNL-r_G/view
      - >-
        The final exam for the Probability & Statistics course, offered by the
        general department, from 2021, is available at the following link:
        [https://drive.google.com/drive/u/2/folders/1c2w87tPBcFazujOmQ1ZKmiuR__EIsQd3].
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-distilroberta-v1
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: ai college validation
          type: ai-college-validation
        metrics:
          - type: cosine_accuracy@1
            value: 0.55078125
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.82421875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.890625
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.95703125
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.55078125
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27473958333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17812499999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.095703125
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.55078125
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.82421875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.890625
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.95703125
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7655983040473691
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7029761904761903
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7052547923124669
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.66015625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9453125
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66015625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31510416666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66015625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9453125
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8528799902335868
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8027994791666668
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8027994791666666
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.66015625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.94140625
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.99609375
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.66015625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3138020833333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19921875
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.66015625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.94140625
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.99609375
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8541928904310672
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8045572916666668
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8045572916666667
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.67578125
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9453125
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.67578125
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31510416666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.67578125
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9453125
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8605213037068725
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8130208333333334
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8130208333333334
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.68359375
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.95703125
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68359375
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31901041666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68359375
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.95703125
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8643861203886329
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8181640625000001
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8181640625
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.68359375
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.95703125
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68359375
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31901041666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68359375
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.95703125
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8655801956151241
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8196614583333336
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8196614583333333
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.69140625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9609375
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.98828125
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.69140625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3203125
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19765625000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.69140625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9609375
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.98828125
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8686343143993309
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8239908854166668
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8239908854166667
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.68359375
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.95703125
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.68359375
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.31901041666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.68359375
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.95703125
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8655801956151241
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8196614583333336
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8196614583333333
            name: Cosine Map@100

SentenceTransformer based on sentence-transformers/all-distilroberta-v1

This is a sentence-transformers model finetuned from sentence-transformers/all-distilroberta-v1. 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: sentence-transformers/all-distilroberta-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Bo8dady/finetuned-College-embeddings")
# Run inference
sentences = [
    'How do I access the final exam for the Digital Image Processing course from 2016?',
    'The final exam for Digital Image Processing course, offered by the computer science department, from 2016, is available at the following link: [https://drive.google.com/file/d/1dUDU-VM5_c7Wst98iTC83GhudfNL-r_G/view',
    'The final exam for the Statistical Analysis course, offered by the general department, from 2025, is available at the following link: [https://drive.google.com/file/d/14Fi9uMdy0JRw7Wp2j1-2eNoRd5CwS_ng/view?usp=sharing',
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5508
cosine_accuracy@3	0.8242
cosine_accuracy@5	0.8906
cosine_accuracy@10	0.957
cosine_precision@1	0.5508
cosine_precision@3	0.2747
cosine_precision@5	0.1781
cosine_precision@10	0.0957
cosine_recall@1	0.5508
cosine_recall@3	0.8242
cosine_recall@5	0.8906
cosine_recall@10	0.957
cosine_ndcg@10	0.7656
cosine_mrr@10	0.703
cosine_map@100	0.7053

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6602
cosine_accuracy@3	0.9453
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6602
cosine_precision@3	0.3151
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6602
cosine_recall@3	0.9453
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8529
cosine_mrr@10	0.8028
cosine_map@100	0.8028

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6602
cosine_accuracy@3	0.9414
cosine_accuracy@5	0.9961
cosine_accuracy@10	1.0
cosine_precision@1	0.6602
cosine_precision@3	0.3138
cosine_precision@5	0.1992
cosine_precision@10	0.1
cosine_recall@1	0.6602
cosine_recall@3	0.9414
cosine_recall@5	0.9961
cosine_recall@10	1.0
cosine_ndcg@10	0.8542
cosine_mrr@10	0.8046
cosine_map@100	0.8046

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6758
cosine_accuracy@3	0.9453
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6758
cosine_precision@3	0.3151
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6758
cosine_recall@3	0.9453
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8605
cosine_mrr@10	0.813
cosine_map@100	0.813

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6836
cosine_accuracy@3	0.957
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6836
cosine_precision@3	0.319
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6836
cosine_recall@3	0.957
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8644
cosine_mrr@10	0.8182
cosine_map@100	0.8182

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6836
cosine_accuracy@3	0.957
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6836
cosine_precision@3	0.319
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6836
cosine_recall@3	0.957
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8656
cosine_mrr@10	0.8197
cosine_map@100	0.8197

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.9609
cosine_accuracy@5	0.9883
cosine_accuracy@10	1.0
cosine_precision@1	0.6914
cosine_precision@3	0.3203
cosine_precision@5	0.1977
cosine_precision@10	0.1
cosine_recall@1	0.6914
cosine_recall@3	0.9609
cosine_recall@5	0.9883
cosine_recall@10	1.0
cosine_ndcg@10	0.8686
cosine_mrr@10	0.824
cosine_map@100	0.824

Information Retrieval

Dataset: ai-college-validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6836
cosine_accuracy@3	0.957
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.6836
cosine_precision@3	0.319
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.6836
cosine_recall@3	0.957
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8656
cosine_mrr@10	0.8197
cosine_map@100	0.8197

Training Details

Training Dataset

Unnamed Dataset

Size: 2,048 training samples
Columns: Question and chunk
Approximate statistics based on the first 1000 samples:
Question chunk
type string string
details
min: 10 tokens
mean: 15.84 tokens
max: 25 tokens

min: 25 tokens
mean: 84.15 tokens
max: 467 tokens

	Question	chunk
type	string	string
details	min: 10 tokens mean: 15.84 tokens max: 25 tokens	min: 25 tokens mean: 84.15 tokens max: 467 tokens

Samples:

Question	chunk
`Could you share the link to the 2020 Data Structures final exam?`	`The final exam for Data Structures course, offered by the general department, from 2020, is available at the following link: [https://drive.google.com/file/d/1U735N5tPHTyXtWgoSp0XI1zo9j2LN2Km/view`
`Can you provide the exam link for the 2018 Software Engineering course?`	`The final exam for Software Engineering course, offered by the computer science department, from 2018, is available at the following link: [https://drive.google.com/file/d/1kqjCVWTBJVhr_JyiTmfrK1BrHy8_tVX2/view`
`- Who decides if an absence excuse is acceptable for a final exam?`	`Topic: Absence from Written Exam Summary: Unexcused absence from a final exam results in a failing grade (F). Chunk: "Absence from the written exam A student who is absent from the final exam for a course without an acceptable excuse from the College Council is considered a failure in the course and has a grade (F)."`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 256 evaluation samples
Columns: Question and chunk
Approximate statistics based on the first 256 samples:
Question chunk
type string string
details
min: 10 tokens
mean: 16.01 tokens
max: 25 tokens

min: 27 tokens
mean: 79.97 tokens
max: 467 tokens

	Question	chunk
type	string	string
details	min: 10 tokens mean: 16.01 tokens max: 25 tokens	min: 27 tokens mean: 79.97 tokens max: 467 tokens

Samples:

Question	chunk
`How do I get to the final exam for the AI course in 2016?`	`The final exam for Artificial Intelligence course, offered by the general department, from 2016, is available at the following link: [https://drive.google.com/file/d/1vaZOQMuqe4qfzPzgvxiSz0rnGxzFwL-F/view?usp=sharing`
`Can I get the URL for the 2024 Probability and Statistics final exam?`	`The final exam for the Probability & Statistics course, offered by the general department, from 2024, is available at the following link: [https://drive.google.com/file/d/1lAFwZRcgDl02zKwrFclAvmqr5k9Z_Ct2/view?usp=sharing].`
`Where can I find the final exam link for the Digital Signal Processing course from 2024?`	`The final exam for Digital Signal Processing course, offered by the computer science department, from 2024, is available at the following link: [https://drive.google.com/file/d/1RO0aPoom-TA-qgsopwR9krszD_pQIzfJ/view?usp=sharing`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1e-05
warmup_ratio: 0.2
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 32
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: 1e-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: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
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: False
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: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	ai-college-validation_cosine_ndcg@10
0	0	-	-	0.7656
1.0	64	-	-	0.8542
1.5469	100	0.0359	0.0239	0.8529
2.9688	192	-	-	0.8575
1.5469	100	0.0126	0.0306	0.8621
3.0781	200	0.0155	0.0267	0.8575
4.625	300	0.0195	0.0287	0.8542
4.9375	320	-	-	0.8556
1.5469	100	0.0034	0.0289	0.8605
2.9688	192	-	-	0.8615
1.5469	100	0.0014	0.0312	0.8644
2.9688	192	-	-	0.8656

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.0
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.3.1
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}