Upload fine-tuned chart reranker model

README.md

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+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:6851
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- pearson
+- spearman
+model-index:
+- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
+  results:
+  - task:
+      type: cross-encoder-correlation
+      name: Cross Encoder Correlation
+    dataset:
+      name: validation
+      type: validation
+    metrics:
+    - type: pearson
+      value: 0.6742730018723011
+      name: Pearson
+    - type: spearman
+      value: 0.5158175772359095
+      name: Spearman
+---
+
+# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("cross_encoder_model_id")
+# Get scores for pairs of texts
+pairs = [
+    ['According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?', 'Title: "Life satisfaction, measured weekly (United Kingdom)"\n  Collections: YouGov Trackers\n  Datasets: YouGovTrackerValueV2\n  Chart Type: survey:timeseries\n  Sources: YouGov'],
+    ['Scoreline for Al‑Bayraq W vs Al‑Riyadh W (WFDL)', 'Title: "Grainger Overview, CBSE:IAM Overview"\n  Collections: Companies\n  Datasets: InstrumentClosePrice1Day\n  Chart Type: timeseries'],
+    ["According to the article 'Top 3 Higher Education Trends to Watch in 2025' by Hanover Research, what percentage of prospective college students in the U.S. report feeling 'not at all familiar' or only 'slightly familiar' with the application process?", 'Title: "AirTanker Services Limited Percentage"\n  Collections: Companies\n  Chart Type: company_card\n  Company: name=ATS Corporation, aliases=[\'ATS Automation Tooling Systems Inc.\', \'Ats Corp\', \'ATS\']\n  Sources: S&P Global'],
+    ["When did RetailMeNot launch the '5 to Buy' event?", 'Title: "Art - past 3 months (United States)"\n  Collections: YouGov Trackers\n  Datasets: YouGovTrackerValueV2\n  Chart Type: survey:timeseries\n  Sources: YouGov]'],
+    ["When was the article '5 Key Trends To Shape Your Business Strategy For 2025' by IESE Business School published on Forbes?", 'Title: "Business Coach Overview"\n  Collections: Companies\n  Chart Type: company_card\n  Company: name=Business Coach Inc., aliases=[\'Business Coach\']\n  Sources: S&P Global'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?',
+    [
+        'Title: "Life satisfaction, measured weekly (United Kingdom)"\n  Collections: YouGov Trackers\n  Datasets: YouGovTrackerValueV2\n  Chart Type: survey:timeseries\n  Sources: YouGov',
+        'Title: "Grainger Overview, CBSE:IAM Overview"\n  Collections: Companies\n  Datasets: InstrumentClosePrice1Day\n  Chart Type: timeseries',
+        'Title: "AirTanker Services Limited Percentage"\n  Collections: Companies\n  Chart Type: company_card\n  Company: name=ATS Corporation, aliases=[\'ATS Automation Tooling Systems Inc.\', \'Ats Corp\', \'ATS\']\n  Sources: S&P Global',
+        'Title: "Art - past 3 months (United States)"\n  Collections: YouGov Trackers\n  Datasets: YouGovTrackerValueV2\n  Chart Type: survey:timeseries\n  Sources: YouGov]',
+        'Title: "Business Coach Overview"\n  Collections: Companies\n  Chart Type: company_card\n  Company: name=Business Coach Inc., aliases=[\'Business Coach\']\n  Sources: S&P Global',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### Cross Encoder Correlation
+
+* Dataset: `validation`
+* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| pearson      | 0.6743     |
+| **spearman** | **0.5158** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 6,851 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                                     | sentence_1                                                                                       | label                                                          |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | float                                                          |
+  | details | <ul><li>min: 7 characters</li><li>mean: 99.0 characters</li><li>max: 2253 characters</li></ul> | <ul><li>min: 79 characters</li><li>mean: 184.27 characters</li><li>max: 716 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                             | sentence_1                                                                                                                                                                                                                                            | label            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>According to a study by the Global Sustainable Tourism Council, by what percentage can sustainable tourism practices increase visitor satisfaction?</code>                                                                                                       | <code>Title: "Life satisfaction, measured weekly (United Kingdom)"<br>  Collections: YouGov Trackers<br>  Datasets: YouGovTrackerValueV2<br>  Chart Type: survey:timeseries<br>  Sources: YouGov</code>                                               | <code>0.0</code> |
+  | <code>Scoreline for Al‑Bayraq W vs Al‑Riyadh W (WFDL)</code>                                                                                                                                                                                                           | <code>Title: "Grainger Overview, CBSE:IAM Overview"<br>  Collections: Companies<br>  Datasets: InstrumentClosePrice1Day<br>  Chart Type: timeseries</code>                                                                                            | <code>0.0</code> |
+  | <code>According to the article 'Top 3 Higher Education Trends to Watch in 2025' by Hanover Research, what percentage of prospective college students in the U.S. report feeling 'not at all familiar' or only 'slightly familiar' with the application process?</code> | <code>Title: "AirTanker Services Limited Percentage"<br>  Collections: Companies<br>  Chart Type: company_card<br>  Company: name=ATS Corporation, aliases=['ATS Automation Tooling Systems Inc.', 'Ats Corp', 'ATS']<br>  Sources: S&P Global</code> | <code>0.0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fn": "torch.nn.modules.linear.Identity",
+      "pos_weight": null
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 1
+
+#### 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`: 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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | validation_spearman |
+|:------:|:----:|:-------------------:|
+| 0.2326 | 50   | 0.3960              |
+| 0.4651 | 100  | 0.4804              |
+| 0.6977 | 150  | 0.5031              |
+| 0.9302 | 200  | 0.5158              |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.1
+- PyTorch: 2.9.0
+- Accelerate: 1.10.1
+- Datasets: 4.2.0
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.1"
+  },
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval/CrossEncoderCorrelationEvaluator_validation_results.csv

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+epoch,steps,Pearson_Correlation,Spearman_Correlation
+1.0,215,0.6765057885942694,0.5160340950125839

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:04a6402495d00a3b98e802a2e1ce50fada050156fa8ac9906d5c561e9fd2aec2
+size 90866412

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_info.txt

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+Base Model: cross-encoder/ms-marco-MiniLM-L6-v2
+Training Samples: 6851
+Epochs: 1
+Batch Size: 32
+Learning Rate: 2e-05
+Max Length: 512