--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:98660 - loss:MultipleNegativesRankingLoss base_model: intfloat/multilingual-e5-base widget: - source_sentence: 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context Dialogue Context: bot_0: Do you like gaming. I am a big fan. bot_1: My kids play games but I don''t play much. I love to watch movies!. bot_0: Oh really what is their favorite game? bot_1: I think it''s called fortnite. I sometimes watch while cooking healthy meals. What''s yours? bot_0: The best game I like to play is alistar. bot_1: Never heard of it. Old timer here! Just turned 30. What other things do you like?' sentences: - 'Followup phrase: I usually only eat them when my kids want them, it''s not something that I''ll make for myself. What''s your favorite dip for chicken nuggets?' - 'Followup phrase: My big doberman lays on me all the time and ripped mine off' - 'Followup phrase: Yeah, he also got me into cars.' - source_sentence: 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context Dialogue Context: bot_0: Just sitting down to dinner after work. Steak! bot_1: Listening to my beethoven favorite, moonlight sonata.. bot_0: Nice! I listen to music at work a lot. What do you do? bot_1: I practice shooting with both of my handgunds and watch british tv. You? bot_0: Sales. The playlist of black sabbath usually pumps me up to sell! Lol. bot_1: My grandma from italy came to visit, and iron man is her favorite song! bot_0: Your grandma rocks! Love italy, hope to visit but need to pay off some debt first. bot_1: I understand that. I want to travel in general but I can''t at the moment.. bot_0: Hopefully you will! I’m so focused on my career, travel is a low priority at this point. bot_1: Same for me! I barely paid off my volkswagen beetle. bot_0: Love that car. What color?' sentences: - 'Followup phrase: I hope so. I just try to keep positive, eat healthy and drink lots of water.' - 'Followup phrase: I just made a seafood chowder lately! It tastes great. What''s your favourite dish to cook at your restuarant?' - 'Followup phrase: Do you speak any other languages? I enjoy learning them.' - source_sentence: 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context Dialogue Context: bot_0: Hello how are you doing today? bot_1: Very well thank you. How are you? bot_0: Going to head out soon to play some baseball. I really like the game.' sentences: - 'Followup phrase: It teaches discipline too. I''m an er nurse so I don''t see my son that much' - 'Followup phrase: I take a boat to work! What about you?' - 'Followup phrase: Yes 3 but they live out of state.. You?' - source_sentence: 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context Dialogue Context: bot_0: Hello, I am in college for marketing. What do you do? bot_1: Hi. Right now an entrepreneur, freelance. I was an accountant before. bot_0: Cool, did you not like being an accountant? bot_1: Not really, I am ready for a new life, new career. Do you have a job? bot_0: No, but I am hoping to design ads one day!' sentences: - 'Followup phrase: Nice. Any pets? I have a dog, he is my best friend..' - 'Followup phrase: Yes! I like to have a little "me" time in the morning to play games before I have to get up for work. It''s so relaxing. When do you usually play games?' - 'Followup phrase: I am a full time student but I work construction in the summer months for' - source_sentence: 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context Dialogue Context: bot_0: Hello, I just got back from class. What are you doing? bot_1: I just got done working out at the gym. bot_0: Cool, what is your favorite exercise? bot_1: Do you have your own vehicle? bot_0: No, I am a student. I walk everywhere or I take the bus. bot_1: Oh wow, that must get tiring. Do you have a significant other? bot_0: It''s not, I even have energy to play baseball. I do not, I am single. bot_1: Thats awesome that you have the energy. My significant other is a lawyer. We''re married.. bot_0: Awe, I hope to have a job designing ads one day. bot_1: That sounds neat. Are you a vegetarian? bot_0: No, but have thought about it!' sentences: - 'Followup phrase: I do not. My husband wants a boy, he is in the army.' - 'Followup phrase: I am amazing, except I found out I am allergic to fish!' - 'Followup phrase: Yeah they can be, single with no kids, which is great!! Living off the land' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-base results: - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9324928469241774 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6963315010070801 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7932711614832003 name: Cosine F1 - type: cosine_f1_threshold value: 0.6896486282348633 name: Cosine F1 Threshold - type: cosine_precision value: 0.791752026365013 name: Cosine Precision - type: cosine_recall value: 0.7947961373390557 name: Cosine Recall - type: cosine_ap value: 0.8751572160892609 name: Cosine Ap - type: cosine_mcc value: 0.7518321554060445 name: Cosine Mcc --- # SentenceTransformer based on intfloat/multilingual-e5-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### 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: XLMRobertaModel (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("sentence_transformers_model_id") # Run inference sentences = [ "Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: bot_0: Hello, I just got back from class. What are you doing?\nbot_1: I just got done working out at the gym.\nbot_0: Cool, what is your favorite exercise?\nbot_1: Do you have your own vehicle?\nbot_0: No, I am a student. I walk everywhere or I take the bus.\nbot_1: Oh wow, that must get tiring. Do you have a significant other?\nbot_0: It's not, I even have energy to play baseball. I do not, I am single.\nbot_1: Thats awesome that you have the energy. My significant other is a lawyer. We're married..\nbot_0: Awe, I hope to have a job designing ads one day.\nbot_1: That sounds neat. Are you a vegetarian?\nbot_0: No, but have thought about it!", 'Followup phrase: I do not. My husband wants a boy, he is in the army.', 'Followup phrase: I am amazing, except I found out I am allergic to fish!', ] 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 #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9325 | | cosine_accuracy_threshold | 0.6963 | | cosine_f1 | 0.7933 | | cosine_f1_threshold | 0.6896 | | cosine_precision | 0.7918 | | cosine_recall | 0.7948 | | **cosine_ap** | **0.8752** | | cosine_mcc | 0.7518 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 98,660 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: What kind of car do you own? I have a jeep. | Followup phrase: I don't own my own car! I actually really enjoying walking and running, but then again, I live in a small town and semi-close to work. | | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: What kind of car do you own? I have a jeep.
bot_1: I don't own my own car! I actually really enjoying walking and running, but then again, I live in a small town and semi-close to work. | Followup phrase: Ah I see! I like going to the gym to work out. | | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: What kind of car do you own? I have a jeep.
bot_1: I don't own my own car! I actually really enjoying walking and running, but then again, I live in a small town and semi-close to work.
bot_0: Ah I see! I like going to the gym to work out. | Followup phrase: I'm a computer programmer. What do you do for work. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 100, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 67,104 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: Do you like music? | Followup phrase: Yes, you could say it is a great source of joy for me. | 1 | | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: Do you like music? | Followup phrase: That sounds amazing! But I was thinking of going to mexico this summer and was going to ask if you were going to be there? Would your timeshare be available? | 0 | | Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context
Dialogue Context: bot_0: Do you like music? | Followup phrase: Mostly just authentic mexican food, with lots of spice. | 0 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 100, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 100 - `weight_decay`: 0.01 - `num_train_epochs`: 5 - `bf16`: True - `load_best_model_at_end`: True - `prompts`: {'sentence1': 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: ', 'sentence2': 'Followup phrase: '} - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 100 - `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`: 0.01 - `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`: 0.0 - `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`: True - `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`: True - `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 - `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`: {'sentence1': 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: ', 'sentence2': 'Followup phrase: '} - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_ap | |:------:|:----:|:-------------:|:---------------:|:---------:| | 0.1013 | 100 | 1.8292 | - | - | | 0.2026 | 200 | 1.4433 | - | - | | 0.3040 | 300 | 1.2605 | - | - | | 0.4053 | 400 | 1.1947 | - | - | | 0.5066 | 500 | 1.1714 | - | - | | 0.6079 | 600 | 1.1106 | - | - | | 0.7092 | 700 | 1.0978 | - | - | | 0.8105 | 800 | 1.0527 | - | - | | 0.9119 | 900 | 1.0524 | - | - | | 1.0 | 987 | - | 8.1109 | 0.8790 | | 1.0132 | 1000 | 1.0068 | - | - | | 1.1145 | 1100 | 0.949 | - | - | | 1.2158 | 1200 | 0.9519 | - | - | | 1.3171 | 1300 | 0.9364 | - | - | | 1.4184 | 1400 | 0.9253 | - | - | | 1.5198 | 1500 | 0.9724 | - | - | | 1.6211 | 1600 | 0.9227 | - | - | | 1.7224 | 1700 | 0.9169 | - | - | | 1.8237 | 1800 | 0.9146 | - | - | | 1.9250 | 1900 | 0.9029 | - | - | | 2.0 | 1974 | - | 8.4529 | 0.8727 | | 2.0263 | 2000 | 0.9073 | - | - | | 2.1277 | 2100 | 0.8685 | - | - | | 2.2290 | 2200 | 0.8413 | - | - | | 2.3303 | 2300 | 0.8763 | - | - | | 2.4316 | 2400 | 0.8524 | - | - | | 2.5329 | 2500 | 0.8729 | - | - | | 2.6342 | 2600 | 0.856 | - | - | | 2.7356 | 2700 | 0.8652 | - | - | | 2.8369 | 2800 | 0.8768 | - | - | | 2.9382 | 2900 | 0.8477 | - | - | | 3.0 | 2961 | - | 8.7662 | 0.8752 | ### Framework Versions - Python: 3.10.18 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cu128 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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", } ``` #### 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} } ```