--- base_model: sentence-transformers/all-mpnet-base-v2 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:60 - loss:CosineSimilarityLoss widget: - source_sentence: '#1# CLCLT00236B - VM not ready | Total Site IDs = 1|Market Affected: CLCLT00236B Reported by: Health check Impact: UE''s roam Full Problem Description: CLCLT00236A - VM not ready External Ticket: N/A Bridge: https://meet.google.com/oab-hmxd-mqb What groups are engaged: VMware Next Action: Assigned the ticket to VMware' sentences: - Precision Time Protocol (PTP) unlocked - Samsung DU Nodes not healthy - VMware VM issue - source_sentence: '#1# - Nodes Not Healthy, Vendor DU pods count is same as 6 | Total Site IDs = 1|Reported by & Contact: Vendor Hypercare Report Impact: UE''s will roam What groups are engaged: NOC Full issue description: Nodes Not Healthy, Vendor DU pods count is not 6' sentences: - Site Sensor temperature alert - PRACH zero - Vendor DU Pods not count not 6 - source_sentence: ' - PTP Unlocked Impact: UE''s will roam What groups are engaged: NOCoE Full issue description: -PTP Unlocked' sentences: - DU Health reported PTP unlocked - DU PTP unlocked - Physical Random access channel value is reported 0 model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8503399836889165 name: Pearson Cosine - type: spearman_cosine value: 0.8646819693607537 name: Spearman Cosine - type: pearson_manhattan value: 0.8610822762797875 name: Pearson Manhattan - type: spearman_manhattan value: 0.8632509605462457 name: Spearman Manhattan - type: pearson_euclidean value: 0.8627648815882912 name: Pearson Euclidean - type: spearman_euclidean value: 0.8646819693607537 name: Spearman Euclidean - type: pearson_dot value: 0.8503399881242814 name: Pearson Dot - type: spearman_dot value: 0.8646819693607537 name: Spearman Dot - type: pearson_max value: 0.8627648815882912 name: Pearson Max - type: spearman_max value: 0.8646819693607537 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the csv dataset. 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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: ```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("yudude/all-mpnet-base-v2-sts") # Run inference sentences = [ " - PTP Unlocked|Reported by & Contact # DU Health Check\nImpact: UE's will roam What groups are engaged: NOCoE\nFull issue description: -PTP Unlocked", 'DU Health reported PTP unlocked', 'DU PTP unlocked', ] 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 #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8503 | | **spearman_cosine** | **0.8647** | | pearson_manhattan | 0.8611 | | spearman_manhattan | 0.8633 | | pearson_euclidean | 0.8628 | | spearman_euclidean | 0.8647 | | pearson_dot | 0.8503 | | spearman_dot | 0.8647 | | pearson_max | 0.8628 | | spearman_max | 0.8647 | ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 60 training samples * Columns: description, search_key, and label * Approximate statistics based on the first 60 samples: | | description | search_key | label | |:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | description | search_key | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:------------------| | UE can not camp on network (drive test)|RU Healthcheck is okay | Network drive test shows UE cannot attach | 0.98 | | Samsung Alert : UADPF: 12345 (AAA) - service-off at /0725C-NR | UADPF Service off issue | 0.95 | | Samsung Alert : UADPF: 12345 (AAA) - - service-off at 0725C-NR | Vendor UADPF service off issue | 0.94 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### csv * Dataset: csv * Size: 12 evaluation samples * Columns: description, search_key, and label * Approximate statistics based on the first 12 samples: | | description | search_key | label | |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | description | search_key | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:------------------| | Temperature Sensor Fault ALERT | | with Temperature: Max cell ST1 29.4 | Max cell ST2 | Min cell ST1 -3276.8 | Min cell ST2
Temperature: 29
Sitename :TESTSITE | Site Sensor temperature alert | 0.96 | | - PTP Unlocked|Reported by & Contact # DU Health Check
Impact: UE's will roam
Bridge: https://meet.google.com/oab-hmxd-qsa
What groups are engaged: NOCoE
Full issue description: -PTP Unlocked | Precision Time Protocol (PTP) unlocked | 0.94 | | - PTP Unlocked|Reported by & Contact # DU Health Check
Impact: UE's will roam
Bridge: https://meet.google.com/oab-hmxd-qsa
What groups are engaged: NOCoE
Full issue description: -PTP Unlocked | DU PTP unlocked | 0.96 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `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`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-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`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `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 - `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`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | |:------:|:----:|:-------------:|:---------------:|:-----------------------:| | 0.2667 | 4 | 0.2285 | 0.1834 | 0.8813 | | 0.5333 | 8 | 0.1028 | 0.0760 | 0.8815 | | 0.8 | 12 | 0.0409 | 0.0240 | 0.8803 | | 1.0667 | 16 | 0.0235 | 0.0080 | 0.8781 | | 1.3333 | 20 | 0.0077 | 0.0023 | 0.8750 | | 1.6 | 24 | 0.0031 | 0.0010 | 0.8721 | | 1.8667 | 28 | 0.0009 | 0.0006 | 0.8697 | | 2.1333 | 32 | 0.0006 | 0.0006 | 0.8678 | | 2.4 | 36 | 0.0006 | 0.0006 | 0.8667 | | 2.6667 | 40 | 0.0009 | 0.0006 | 0.8660 | | 2.9333 | 44 | 0.0004 | 0.0006 | 0.8654 | | 3.2 | 48 | 0.0007 | 0.0006 | 0.8651 | | 3.4667 | 52 | 0.0006 | 0.0006 | 0.8649 | | 3.7333 | 56 | 0.0005 | 0.0006 | 0.8648 | | 4.0 | 60 | 0.0003 | 0.0006 | 0.8647 | | 4.2667 | 64 | 0.0007 | 0.0006 | 0.8647 | | 4.5333 | 68 | 0.0005 | 0.0006 | 0.8647 | | 4.8 | 72 | 0.0006 | 0.0006 | 0.8647 | ### 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: 3.1.0 - 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", } ```