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itsanan/codebert-finetuned-crewai-base

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face

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CodeBERT Fine-tuned on CrewAI (LR=2e-05)

This is a sentence-transformers model finetuned from microsoft/codebert-base. 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/codebert-base
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 768 dimensions
—Similarity Function: Cosine Similarity
—Language: en
—License: apache-2.0

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, 'architecture': '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})
)

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("itsanan/codebert-finetuned-crewai-base")
# Run inference
sentences = [
    'Example usage of test_status_code_and_content_type',
    'def test_status_code_and_content_type(self, mock_bs, mock_get):\n        for status in [200, 201, 301]:\n            mock_get.return_value = self.setup_mock_response(\n                f"<html><body>Status {status}</body></html>", status_code=status\n            )\n            mock_bs.return_value = self.setup_mock_soup(f"Status {status}")\n            result = WebPageLoader().load(\n                SourceContent(f"https://example.com/{status}")\n            )\n            assert result.metadata["status_code"] == status\n\n        for ctype in ["text/html", "text/plain", "application/xhtml+xml"]:\n            mock_get.return_value = self.setup_mock_response(\n                "<html><body>Content</body></html>", content_type=ctype\n            )\n            mock_bs.return_value = self.setup_mock_soup("Content")\n            result = WebPageLoader().load(SourceContent("https://example.com"))\n            assert result.metadata["content_type"] == ctype',
    'def set_crew(self, crew: Any) -> Memory:\n        """Set the crew for this memory instance."""\n        self.crew = crew\n        return self',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9009, 0.9087],
#         [0.9009, 1.0000, 0.9053],
#         [0.9087, 0.9053, 1.0000]])

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Evaluation

Metrics

Information Retrieval

—Dataset: dim_768
—Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

json

  {
      "truncate_dim": 768
  }

Metric	Value
cosine_accuracy@1	0.04
cosine_accuracy@3	0.04
cosine_accuracy@5	0.04
cosine_accuracy@10	0.06
cosine_precision@1	0.04
cosine_precision@3	0.04
cosine_precision@5	0.04
cosine_precision@10	0.03
cosine_recall@1	0.008
cosine_recall@3	0.024
cosine_recall@5	0.04
cosine_recall@10	0.06
cosine_ndcg@10	0.0508
cosine_mrr@10	0.0433
cosine_map@100	0.0613

Information Retrieval

—Dataset: dim_512
—Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

json

  {
      "truncate_dim": 512
  }

Metric	Value
cosine_accuracy@1	0.01
cosine_accuracy@3	0.01
cosine_accuracy@5	0.01
cosine_accuracy@10	0.01
cosine_precision@1	0.01
cosine_precision@3	0.01
cosine_precision@5	0.01
cosine_precision@10	0.005
cosine_recall@1	0.002
cosine_recall@3	0.006
cosine_recall@5	0.01
cosine_recall@10	0.01
cosine_ndcg@10	0.01
cosine_mrr@10	0.01
cosine_map@100	0.0193

Information Retrieval

—Dataset: dim_256
—Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

json

  {
      "truncate_dim": 256
  }

Metric	Value
cosine_accuracy@1	0.01
cosine_accuracy@3	0.01
cosine_accuracy@5	0.01
cosine_accuracy@10	0.03
cosine_precision@1	0.01
cosine_precision@3	0.01
cosine_precision@5	0.01
cosine_precision@10	0.015
cosine_recall@1	0.002
cosine_recall@3	0.006
cosine_recall@5	0.01
cosine_recall@10	0.03
cosine_ndcg@10	0.0208
cosine_mrr@10	0.0133
cosine_map@100	0.029

Information Retrieval

—Dataset: dim_128
—Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

json

  {
      "truncate_dim": 128
  }

Metric	Value
cosine_accuracy@1	0.01
cosine_accuracy@3	0.01
cosine_accuracy@5	0.01
cosine_accuracy@10	0.01
cosine_precision@1	0.01
cosine_precision@3	0.01
cosine_precision@5	0.01
cosine_precision@10	0.005
cosine_recall@1	0.002
cosine_recall@3	0.006
cosine_recall@5	0.01
cosine_recall@10	0.01
cosine_ndcg@10	0.01
cosine_mrr@10	0.01
cosine_map@100	0.0275

Information Retrieval

—Dataset: dim_64
—Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

json

  {
      "truncate_dim": 64
  }

Metric	Value
cosine_accuracy@1	0.05
cosine_accuracy@3	0.05
cosine_accuracy@5	0.05
cosine_accuracy@10	0.07
cosine_precision@1	0.05
cosine_precision@3	0.05
cosine_precision@5	0.05
cosine_precision@10	0.035
cosine_recall@1	0.01
cosine_recall@3	0.03
cosine_recall@5	0.05
cosine_recall@10	0.07
cosine_ndcg@10	0.0608
cosine_mrr@10	0.0533
cosine_map@100	0.0839

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Training Details

Training Dataset

Unnamed Dataset

—Size: 900 training samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 900 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 13.86 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 253.07 tokens</li><li>max: 512 tokens</li></ul> |
—Samples: | anchor | positive | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>How to implement LLMCallCompletedEvent?</code> | <code>class LLMCallCompletedEvent(LLMEventBase): """Event emitted when a LLM call completes""" type: str = "llmcallcompleted" messages: str \| list[dict[str, Any]] \| None = None response: Any calltype: LLMCallType model: str \| None = None</code> | | <code>How does getllmresponse work in Python?</code> | <code>def getllmresponse( llm: LLM \| BaseLLM, messages: list[LLMMessage], callbacks: list[TokenCalcHandler], printer: Printer, fromtask: Task \| None = None, fromagent: Agent \| LiteAgent \| None = None, responsemodel: type[BaseModel] \| None = None, executorcontext: CrewAgentExecutor \| LiteAgent \| None = None, ) -> str: """Call the LLM and return the response, handling any invalid responses. Args: llm: The LLM instance to call. messages: The messages to send to the LLM. callbacks: List of callbacks for the LLM call. printer: Printer instance for output. fromtask: Optional task context for the LLM call. fromagent: Optional agent context for the LLM call. responsemodel: Optional Pydantic model for structured outputs. executorcontext: Optional executor context for hook invocation. Returns: The response from the LLM as a string. Raises: Exception: If an error ...</code> | | <code>Example usage of run</code> | <code>def run( self, **kwargs: Any, ) -> Any: websiteurl: str \| None = kwargs.get("websiteurl", self.websiteurl) if websiteurl is None: raise ValueError("Website URL must be provided.") page = requests.get( websiteurl, timeout=15, headers=self.headers, cookies=self.cookies if self.cookies else {}, ) page.encoding = page.apparentencoding parsed = BeautifulSoup(page.text, "html.parser") text = "The following text is scraped website content:\n\n" text += parsed.gettext(" ") text = re.sub("[ \t]+", " ", text) return re.sub("\\s+\n\\s+", "\n", text)</code> |
—Loss: <code>MatryoshkaLoss</code> with these parameters:

json

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—per_device_train_batch_size: 4
—per_device_eval_batch_size: 4
—gradient_accumulation_steps: 32
—learning_rate: 2e-05
—weight_decay: 0.01
—num_train_epochs: 20
—lr_scheduler_type: cosine
—warmup_ratio: 0.1
—fp16: True
—load_best_model_at_end: True
—optim: adamw_torch
—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: 4
—per_device_eval_batch_size: 4
—per_gpu_train_batch_size: None
—per_gpu_eval_batch_size: None
—gradient_accumulation_steps: 32
—eval_accumulation_steps: None
—torch_empty_cache_steps: None
—learning_rate: 2e-05
—weight_decay: 0.01
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1.0
—num_train_epochs: 20
—max_steps: -1
—lr_scheduler_type: cosine
—lr_scheduler_kwargs: None
—warmup_ratio: 0.1
—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
—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: True
—ignore_data_skip: False
—fsdp: []
—fsdp_min_num_params: 0
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamw_torch
—optim_args: None
—adafactor: False
—group_by_length: False
—length_column_name: length
—project: huggingface
—trackio_space_id: trackio
—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
—hub_revision: None
—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
—include_tokens_per_second: False
—include_num_input_tokens_seen: no
—neftune_noise_alpha: None
—optim_target_modules: None
—batch_eval_metrics: False
—eval_on_start: False
—use_liger_kernel: False
—liger_kernel_config: None
—eval_use_gather_object: False
—average_tokens_across_devices: True
—prompts: None
—batch_sampler: no_duplicates
—multi_dataset_batch_sampler: proportional
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.9956	7	-	0.04	0.04	0.03	0.0262	0.0308
1.2844	10	7.098	-	-	-	-	-
1.8533	14	-	0.0362	0.02	0.0354	0.0154	0.0508
2.5689	20	6.5515	-	-	-	-	-
2.7111	21	-	0.0508	0.01	0.0208	0.01	0.0608

—The bold row denotes the saved checkpoint.

Framework Versions

—Python: 3.12.12
—Sentence Transformers: 5.2.2
—Transformers: 4.57.6
—PyTorch: 2.9.0+cu126
—Accelerate: 1.12.0
—Datasets: 4.0.0
—Tokenizers: 0.22.2

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@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}
}

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