| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:4984 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: '<think> |
| |
| Let’s think through this step by step |
| |
| rp = 500/month |
| |
| fp = 10/day |
| |
| lp = 60/lesson |
| |
| lpw = 2 lessons/week |
| |
| yp = 12 months/year |
| |
| total = (500 × 12) + (10 × 365) + (60 × 2 × 52) |
| |
| total = 6000 + 3650 + 6240 |
| |
| total = 15890 |
| |
| </think> |
| |
| \boxed{15890}' |
| sentences: |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| rp = 500/month |
| |
| fp = 10/day |
| |
| lp = 60/lesson |
| |
| tp = (500 × 12) + (10 × 365) + (60 × 2 × 52) |
| |
| tp = 6000 + 3650 + 6240 |
| |
| tp = 15890 |
| |
| </think> |
| |
| \boxed{15890}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| h1 = 500 ft |
| |
| h2 = 2 * h1 = 2 * 500 = 1000 ft |
| |
| Total height = h1 + h2 = 500 + 1000 = 1500 ft |
| |
| </think> |
| |
| \boxed{1500}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| fc = 200 |
| |
| lc = 500 |
| |
| dc = 500 × 0.8 = 400 |
| |
| nc = 200 - 50 = 150 |
| |
| tc = 150 + 400 = 550 |
| |
| </think> |
| |
| \boxed{550}' |
| - source_sentence: '<think> |
| |
| Let’s think through this step by step |
| |
| s = 5 MB/song |
| |
| m1 = 10 songs |
| |
| m2 = 15 songs |
| |
| m3 = 3 songs |
| |
| total = (10 + 15 + 3) × 5 |
| |
| total = 28 × 5 = 140 MB |
| |
| </think> |
| |
| \boxed{140}' |
| sentences: |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| s = 5 MB/song |
| |
| m1 = 10 songs |
| |
| m2 = 15 songs |
| |
| m3 = 3 songs |
| |
| Total memory = (10 + 15 + 3) × 5 |
| |
| Total memory = 28 × 5 = 140 MB |
| |
| </think> |
| |
| \boxed{140}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| total_slices = 12 |
| |
| eaten_slices = 12 × (1/4) = 3 |
| |
| kept_slices = 12 - 3 = 9 |
| |
| </think> |
| |
| \boxed{9}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| Bald Mountain = 1.5 meters = 150 cm |
| |
| Billy Mountain = 3.5 meters = 350 cm |
| |
| Mount Pilot = 126 cm |
| |
| Difference between Billy Mountain and Bald Mountain = 350 - 150 = 200 cm |
| |
| Difference between Mount Pilot and Bald Mountain = 126 - 150 = -24 cm |
| |
| </think> |
| |
| \boxed{200}' |
| - source_sentence: '<think> |
| |
| Let’s think through this step by step |
| |
| Strength training: 1 hour/session × 3 sessions/week = 3 hours/week |
| |
| Boxing training: 1.5 hours/session × 4 sessions/week = 6 hours/week |
| |
| Total training hours per week = 3 + 6 = 9 hours/week |
| |
| </think> |
| |
| \boxed{9}' |
| sentences: |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| t = 5 |
| |
| p = 4 |
| |
| s = 6 |
| |
| r = 5 / 2 = 2.5 |
| |
| income = (2 × 5) + 4 + (4 × 6) + (6 × 2.5) |
| |
| income = 10 + 4 + 24 + 15 |
| |
| income = 53 |
| |
| </think> |
| |
| \boxed{53}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| Strength training: 1 hour/session × 3 sessions/week = 3 hours/week |
| |
| Boxing training: 1.5 hours/session × 4 sessions/week = 6 hours/week |
| |
| Total training hours per week = 3 + 6 = 9 hours/week |
| |
| </think> |
| |
| \boxed{9}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| Let d be the number of ducks sold |
| |
| Earnings from selling chickens = 5 × 8 = 40 |
| |
| Earnings from selling ducks = 10d |
| |
| Total earnings = 40 + 10d |
| |
| Amount spent on wheelbarrow = (1/2)(40 + 10d) |
| |
| Amount earned from selling wheelbarrow = 60 |
| |
| 40 + 10d = 2(60) |
| |
| 10d = 80 |
| |
| d = 8 |
| |
| </think> |
| |
| \boxed{8}' |
| - source_sentence: '<think> |
| |
| Let’s think through this step by step |
| |
| up = 3 flights |
| |
| down = 3 flights |
| |
| total = (3 + 3) × 5 = 30 flights |
| |
| </think> |
| |
| \boxed{30}' |
| sentences: |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| up = 3 flights |
| |
| down = 3 flights |
| |
| total = (3 + 3) × 5 = 30 flights |
| |
| </think> |
| |
| \boxed{30}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| J = M + 10 |
| |
| J + 10 = 25 |
| |
| (M + 10) + 10 = 25 |
| |
| M + 20 = 25 |
| |
| M = 5 |
| |
| J = 5 + 10 = 15 |
| |
| Sum = 5 + 15 = 20 |
| |
| </think> |
| |
| \boxed{20}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| total_money = 7(1) + 4(5) + 2(10) + 1(20) = 7 + 20 + 20 + 20 = 67 |
| |
| cost = 67 - 4 = 63 |
| |
| pounds = 63 / 3 = 21 |
| |
| days = 7 |
| |
| average = 21 / 7 = 3 |
| |
| </think> |
| |
| \boxed{3}' |
| - source_sentence: '<think> |
| |
| Let’s think through this step by step |
| |
| n = 4 |
| |
| sp = 75 |
| |
| t = 36 |
| |
| spt = 36 / 4 = 9 |
| |
| op = 75 + 9 = 84 |
| |
| </think> |
| |
| \boxed{84}' |
| sentences: |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| Let B be Benedict''s house size |
| |
| K = 10000 sq ft |
| |
| K = 4B + 600 |
| |
| 10000 = 4B + 600 |
| |
| 4B = 9400 |
| |
| B = 2350 sq ft |
| |
| </think> |
| |
| \boxed{2350}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| Total throws = 80 |
| |
| No pass thrown = 30% of 80 = 0.3 × 80 = 24 |
| |
| Sacked for a loss = 0.5 × 24 = 12 |
| |
| </think> |
| |
| \boxed{12}' |
| - '<think> |
| |
| Let’s think through this step by step |
| |
| n = 4 |
| |
| sp = 75 |
| |
| t = 36 |
| |
| spt = t / n = 36 / 4 = 9 |
| |
| op = sp + spt = 75 + 9 = 84 |
| |
| </think> |
| |
| \boxed{84}' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts dev |
| type: sts-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.8793284892973376 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.876484495899188 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test |
| type: sts-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.879334132854901 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8764936381058213 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - csv |
| <!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
| (1): Pooling({'word_embedding_dimension': 384, '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("training") |
| # Run inference |
| sentences = [ |
| '<think>\nLet’s think through this step by step\nn = 4\nsp = 75\nt = 36\nspt = 36 / 4 = 9\nop = 75 + 9 = 84\n</think>\n\\boxed{84}', |
| '<think>\nLet’s think through this step by step\nn = 4\nsp = 75\nt = 36\nspt = t / n = 36 / 4 = 9\nop = sp + spt = 75 + 9 = 84\n</think>\n\\boxed{84}', |
| "<think>\nLet’s think through this step by step\nLet B be Benedict's house size\nK = 10000 sq ft\nK = 4B + 600\n10000 = 4B + 600\n4B = 9400\nB = 2350 sq ft\n</think>\n\\boxed{2350}", |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # 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 |
|
|
| * Datasets: `sts-dev` and `sts-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | sts-dev | sts-test | |
| |:--------------------|:-----------|:-----------| |
| | pearson_cosine | 0.8793 | 0.8793 | |
| | **spearman_cosine** | **0.8765** | **0.8765** | |
| |
| <!-- |
| ## 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 |
| |
| #### csv |
| |
| * Dataset: csv |
| * Size: 4,984 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 34 tokens</li><li>mean: 66.41 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 67.03 tokens</li><li>max: 256 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code><think><br>Let’s think through this step by step<br>ht_hare = 14 inches<br>ht_camel = 24 × 14 = 336 inches<br>ht_camel_ft = 336 / 12 = 28 feet<br></think><br>\boxed{28}</code> | <code><think><br>Let’s think through this step by step<br>ht_hare = 14 inches<br>ht_camel = 24 * 14 = 336 inches<br>ht_camel_ft = 336 / 12 = 28 feet<br></think><br>\boxed{28}</code> | |
| | <code><think><br>Let’s think through this step by step<br>cpb = 5<br>sp = 90<br>pmpb = 2<br>tp = 5 × 5 = 25<br>tpmp = 5 × 2 = 10<br>profit = 90 - 25 - 10 = 55<br></think><br>\boxed{55}</code> | <code><think><br>Let’s think through this step by step<br>cp = 5 × 5 = 25<br>sp = 90<br>p = sp - cp - (5 × 2) = 90 - 25 - 10 = 55<br></think><br>\boxed{55}</code> | |
| | <code><think><br>Let’s think through this step by step<br>Time to iron a blouse = 15 minutes<br>Time to iron a dress = 20 minutes<br>Time spent on blouses = 2 hours = 120 minutes<br>Time spent on dresses = 3 hours = 180 minutes<br>Number of blouses ironed = 120 / 15 = 8<br>Number of dresses ironed = 180 / 20 = 9<br>Total pieces of clothes ironed = 8 + 9 = 17<br></think><br>\boxed{17}</code> | <code><think><br>Let’s think through this step by step<br>Time to iron 1 blouse = 15 minutes<br>Time to iron 1 dress = 20 minutes<br>Time spent on blouses = 2 hours = 120 minutes<br>Time spent on dresses = 3 hours = 180 minutes<br>Number of blouses ironed = 120 / 15 = 8<br>Number of dresses ironed = 180 / 20 = 9<br>Total pieces of clothes ironed = 8 + 9 = 17<br></think><br>\boxed{17}</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### csv |
| |
| * Dataset: csv |
| * Size: 4,984 evaluation samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 33 tokens</li><li>mean: 66.68 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 66.71 tokens</li><li>max: 161 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code><think><br>Let’s think through this step by step<br>sg = 36<br>sc = 1/4<br>fl = 1/3<br>sg_left = sg - (sg × sc) - (sg × (1 - sc) × fl)<br>sg_left = 36 - (36 × 1/4) - (36 × (1 - 1/4) × 1/3)<br>sg_left = 36 - 9 - 6<br>sg_left = 21<br></think><br>\boxed{21}</code> | <code><think><br>Let’s think through this step by step<br>sg = 36<br>sc = 1/4<br>fl = 1/3<br>sg_left = sg - (sg × sc) - (sg × (1 - sc) × fl)<br>sg_left = 36 - (36 × 1/4) - (36 × (1 - 1/4) × 1/3)<br>sg_left = 36 - 9 - 6<br>sg_left = 21<br></think><br>\boxed{21}</code> | |
| | <code><think><br>Let’s think through this step by step<br>d1 = 125 miles<br>d2 = 223 miles<br>d3 = 493 - (125 + 223)<br>d3 = 145 miles<br></think><br>\boxed{145}</code> | <code><think><br>Let’s think through this step by step<br>d1 = 125 miles<br>d2 = 223 miles<br>d3 = 493 - (125 + 223)<br>d3 = 145 miles<br></think><br>\boxed{145}</code> | |
| | <code><think><br>Let’s think through this step by step<br>Total workdays = 2 weeks × 5 days/week = 10 days<br>Paid vacation days = 6 days<br>Unpaid vacation days = 10 - 6 = 4 days<br>Total pay = 15 × 8 = $120/day<br>Missed pay = 4 × 120 = $480<br></think><br>\boxed{480}</code> | <code><think><br>Let’s think through this step by step<br>Total workdays = 2 weeks × 5 days/week = 10 days<br>Paid vacation days = 6 days<br>Unpaid vacation days = 10 - 6 = 4 days<br>Total pay = 15 × 8 = $120/day<br>Missed pay = 4 × 120 = $480<br></think><br>\boxed{480}</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 20 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `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`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `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`: 20 |
| - `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} |
| - `tp_size`: 0 |
| - `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`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| |:-------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| |
| | -1 | -1 | - | - | 0.8671 | - | |
| | 0.3953 | 100 | 0.0422 | 0.0031 | 0.8701 | - | |
| | 0.7905 | 200 | 0.0105 | 0.0017 | 0.8727 | - | |
| | 1.1858 | 300 | 0.0041 | 0.0016 | 0.8728 | - | |
| | 1.5810 | 400 | 0.0016 | 0.0011 | 0.8730 | - | |
| | 1.9763 | 500 | 0.0039 | 0.0021 | 0.8731 | - | |
| | 2.3715 | 600 | 0.0014 | 0.0020 | 0.8741 | - | |
| | 2.7668 | 700 | 0.0014 | 0.0017 | 0.8744 | - | |
| | 3.1621 | 800 | 0.0019 | 0.0009 | 0.8742 | - | |
| | 3.5573 | 900 | 0.0012 | 0.0011 | 0.8754 | - | |
| | 3.9526 | 1000 | 0.0016 | 0.0015 | 0.8760 | - | |
| | 4.3478 | 1100 | 0.0021 | 0.0011 | 0.8763 | - | |
| | 4.7431 | 1200 | 0.0006 | 0.0009 | 0.8753 | - | |
| | 5.1383 | 1300 | 0.0004 | 0.0009 | 0.8753 | - | |
| | 5.5336 | 1400 | 0.0008 | 0.0008 | 0.8751 | - | |
| | 5.9289 | 1500 | 0.0004 | 0.0004 | 0.8743 | - | |
| | 6.3241 | 1600 | 0.0009 | 0.0008 | 0.8758 | - | |
| | 6.7194 | 1700 | 0.0005 | 0.0009 | 0.8747 | - | |
| | 7.1146 | 1800 | 0.0004 | 0.0006 | 0.8742 | - | |
| | 7.5099 | 1900 | 0.0003 | 0.0010 | 0.8748 | - | |
| | 7.9051 | 2000 | 0.0006 | 0.0008 | 0.8742 | - | |
| | 8.3004 | 2100 | 0.0005 | 0.0007 | 0.8744 | - | |
| | 8.6957 | 2200 | 0.0003 | 0.0006 | 0.8748 | - | |
| | 9.0909 | 2300 | 0.0005 | 0.0012 | 0.8749 | - | |
| | 9.4862 | 2400 | 0.0007 | 0.0006 | 0.8762 | - | |
| | 9.8814 | 2500 | 0.0003 | 0.0009 | 0.8762 | - | |
| | 10.2767 | 2600 | 0.0004 | 0.0007 | 0.8759 | - | |
| | 10.6719 | 2700 | 0.0005 | 0.0005 | 0.8760 | - | |
| | 11.0672 | 2800 | 0.0005 | 0.0007 | 0.8754 | - | |
| | 11.4625 | 2900 | 0.0002 | 0.0008 | 0.8749 | - | |
| | 11.8577 | 3000 | 0.0002 | 0.0007 | 0.8749 | - | |
| | 12.2530 | 3100 | 0.0003 | 0.0007 | 0.8752 | - | |
| | 12.6482 | 3200 | 0.0004 | 0.0008 | 0.8760 | - | |
| | 13.0435 | 3300 | 0.0002 | 0.0008 | 0.8767 | - | |
| | 13.4387 | 3400 | 0.0002 | 0.0007 | 0.8763 | - | |
| | 13.8340 | 3500 | 0.0002 | 0.0007 | 0.8763 | - | |
| | 14.2292 | 3600 | 0.0001 | 0.0007 | 0.8764 | - | |
| | 14.6245 | 3700 | 0.0003 | 0.0006 | 0.8765 | - | |
| | 15.0198 | 3800 | 0.0002 | 0.0005 | 0.8757 | - | |
| | 15.4150 | 3900 | 0.0002 | 0.0004 | 0.8760 | - | |
| | 15.8103 | 4000 | 0.0002 | 0.0005 | 0.8765 | - | |
| | 16.2055 | 4100 | 0.0002 | 0.0005 | 0.8757 | - | |
| | 16.6008 | 4200 | 0.0002 | 0.0006 | 0.8758 | - | |
| | 16.9960 | 4300 | 0.0002 | 0.0006 | 0.8758 | - | |
| | 17.3913 | 4400 | 0.0001 | 0.0005 | 0.8761 | - | |
| | 17.7866 | 4500 | 0.0002 | 0.0005 | 0.8765 | - | |
| | 18.1818 | 4600 | 0.0001 | 0.0005 | 0.8767 | - | |
| | 18.5771 | 4700 | 0.0004 | 0.0004 | 0.8765 | - | |
| | 18.9723 | 4800 | 0.0002 | 0.0004 | 0.8765 | - | |
| | 19.3676 | 4900 | 0.0001 | 0.0004 | 0.8765 | - | |
| | 19.7628 | 5000 | 0.0001 | 0.0004 | 0.8765 | - | |
| | -1 | -1 | - | - | - | 0.8765 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.11.11 |
| - Sentence Transformers: 3.4.1 |
| - Transformers: 4.51.1 |
| - PyTorch: 2.5.1+cu124 |
| - Accelerate: 1.3.0 |
| - Datasets: 3.5.0 |
| - Tokenizers: 0.21.0 |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## 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.* |
| --> |
