Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/config-checkpoint.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "name": "FineBgem3",
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 293074
+}

.ipynb_checkpoints/config-ori-checkpoint.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 293074
+}

.ipynb_checkpoints/modules-checkpoint.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -1,3 +1,1072 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:377615
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: PACER FDS TR SWAN SOS FLEX JULY ETF(PSFJ)周线级别突破关键阻力位，技术面呈现强势
+  sentences:
+  - 市场解读行业政策对NUVL的积极影响
+  - PACER FDS TR SWAN SOS FLEX JULY ETF(PSFJ)技术指标发出看涨信号，短期或延续涨势
+  - FTXR
+- source_sentence: 行业报告显示CommerceHub(CHUBK)市场份额提升至15%，领跑细分领域
+  sentences:
+  - 据最新行业数据，CommerceHub(CHUBK)市占率增长至15%
+  - 中国智能交通（01900.HK）研发实力获认可，近期获多家机构调研
+  - FPRO
+- source_sentence: PropertyGuru Group Limited Ordinary Shares
+  sentences:
+  - TUYA INC SPON ADS EACH REP 1 CL A ORD SHS(TUYA)股价因消费电子行业复苏周涨幅达4.2%
+  - 物业大师集团
+  - 受保险业务扩张预期推动，THE BALDWIN INSURANCE GRP INC(BWIN)股价上涨逾4%
+- source_sentence: 阿斯特克
+  sentences:
+  - ASTE
+  - 研究指出FinServ Acquisition Corp. II Class A(FSRX)当前估值存在上行空间
+  - 市场波动中RNR-F展现优先股特性，抗风险能力获认可
+- source_sentence: Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价调整引发热议，机构认为或提振短期流动性
+  sentences:
+  - XHLF
+  - HLGN+
+  - 市场关注Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价变动，分析称该调整可能改善短期交易活跃度
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 = [
+    'Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价调整引发热议，机构认为或提振短期流动性',
+    '市场关注Horizon Acquisition Corp. Warrant -on Horizon Acqn(HZAC+)期权行权价变动，分析称该调整可能改善短期交易活跃度',
+    'HLGN+',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.9768, 0.0959],
+#         [0.9768, 1.0000, 0.1028],
+#         [0.0959, 0.1028, 1.0000]])
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## 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: 377,615 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 14.38 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.38 tokens</li><li>max: 60 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                   | sentence_1                                   |
+  |:-----------------------------------------------------------------------------|:---------------------------------------------|
+  | <code>苍南仪表</code>                                                            | <code>苍南自动化仪表</code>                         |
+  | <code>KINS Technology Group, Inc. Warrant 2020- 2025 on KINS Tech Grp</code> | <code>KINZW</code>                           |
+  | <code>兴业合金(00505.HK)技术面呈现多头排列，短期或延续上涨趋势</code>                               | <code>00505.HK兴业合金日线图出现买入信号，技术派看好后市走势</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 30
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `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`: 30
+- `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`: 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_fused
+- `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
+- `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`: False
+- `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`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step   | Training Loss |
+|:-------:|:------:|:-------------:|
+| 0.0424  | 500    | 0.6966        |
+| 0.0847  | 1000   | 0.4987        |
+| 0.1271  | 1500   | 0.463         |
+| 0.1695  | 2000   | 0.4364        |
+| 0.2118  | 2500   | 0.4041        |
+| 0.2542  | 3000   | 0.3923        |
+| 0.2966  | 3500   | 0.3788        |
+| 0.3390  | 4000   | 0.3603        |
+| 0.3813  | 4500   | 0.3442        |
+| 0.4237  | 5000   | 0.3388        |
+| 0.4661  | 5500   | 0.3252        |
+| 0.5084  | 6000   | 0.3133        |
+| 0.5508  | 6500   | 0.311         |
+| 0.5932  | 7000   | 0.3027        |
+| 0.6355  | 7500   | 0.283         |
+| 0.6779  | 8000   | 0.2847        |
+| 0.7203  | 8500   | 0.279         |
+| 0.7626  | 9000   | 0.2753        |
+| 0.8050  | 9500   | 0.2647        |
+| 0.8474  | 10000  | 0.2687        |
+| 0.8898  | 10500  | 0.2572        |
+| 0.9321  | 11000  | 0.2562        |
+| 0.9745  | 11500  | 0.2351        |
+| 1.0169  | 12000  | 0.2254        |
+| 1.0592  | 12500  | 0.1966        |
+| 1.1016  | 13000  | 0.2082        |
+| 1.1440  | 13500  | 0.1856        |
+| 1.1863  | 14000  | 0.1916        |
+| 1.2287  | 14500  | 0.2003        |
+| 1.2711  | 15000  | 0.1959        |
+| 1.3134  | 15500  | 0.1857        |
+| 1.3558  | 16000  | 0.1854        |
+| 1.3982  | 16500  | 0.1797        |
+| 1.4406  | 17000  | 0.1774        |
+| 1.4829  | 17500  | 0.1813        |
+| 1.5253  | 18000  | 0.1717        |
+| 1.5677  | 18500  | 0.1638        |
+| 1.6100  | 19000  | 0.1658        |
+| 1.6524  | 19500  | 0.1764        |
+| 1.6948  | 20000  | 0.1681        |
+| 1.7371  | 20500  | 0.1589        |
+| 1.7795  | 21000  | 0.1539        |
+| 1.8219  | 21500  | 0.1575        |
+| 1.8642  | 22000  | 0.1558        |
+| 1.9066  | 22500  | 0.158         |
+| 1.9490  | 23000  | 0.1467        |
+| 1.9914  | 23500  | 0.1504        |
+| 2.0337  | 24000  | 0.1221        |
+| 2.0761  | 24500  | 0.1112        |
+| 2.1185  | 25000  | 0.109         |
+| 2.1608  | 25500  | 0.1106        |
+| 2.2032  | 26000  | 0.1131        |
+| 2.2456  | 26500  | 0.1078        |
+| 2.2879  | 27000  | 0.1042        |
+| 2.3303  | 27500  | 0.1024        |
+| 2.3727  | 28000  | 0.1012        |
+| 2.4150  | 28500  | 0.1088        |
+| 2.4574  | 29000  | 0.1022        |
+| 2.4998  | 29500  | 0.1067        |
+| 2.5422  | 30000  | 0.105         |
+| 2.5845  | 30500  | 0.0982        |
+| 2.6269  | 31000  | 0.1033        |
+| 2.6693  | 31500  | 0.1029        |
+| 2.7116  | 32000  | 0.0988        |
+| 2.7540  | 32500  | 0.0999        |
+| 2.7964  | 33000  | 0.094         |
+| 2.8387  | 33500  | 0.0912        |
+| 2.8811  | 34000  | 0.0952        |
+| 2.9235  | 34500  | 0.0953        |
+| 2.9659  | 35000  | 0.0947        |
+| 3.0082  | 35500  | 0.0857        |
+| 3.0506  | 36000  | 0.0697        |
+| 3.0930  | 36500  | 0.067         |
+| 3.1353  | 37000  | 0.063         |
+| 3.1777  | 37500  | 0.0673        |
+| 3.2201  | 38000  | 0.067         |
+| 3.2624  | 38500  | 0.0684        |
+| 3.3048  | 39000  | 0.0643        |
+| 3.3472  | 39500  | 0.0656        |
+| 3.3895  | 40000  | 0.0657        |
+| 3.4319  | 40500  | 0.071         |
+| 3.4743  | 41000  | 0.0671        |
+| 3.5167  | 41500  | 0.0601        |
+| 3.5590  | 42000  | 0.0614        |
+| 3.6014  | 42500  | 0.061         |
+| 3.6438  | 43000  | 0.0599        |
+| 3.6861  | 43500  | 0.0586        |
+| 3.7285  | 44000  | 0.0613        |
+| 3.7709  | 44500  | 0.0604        |
+| 3.8132  | 45000  | 0.06          |
+| 3.8556  | 45500  | 0.0539        |
+| 3.8980  | 46000  | 0.0576        |
+| 3.9403  | 46500  | 0.0605        |
+| 3.9827  | 47000  | 0.0563        |
+| 4.0251  | 47500  | 0.0485        |
+| 4.0675  | 48000  | 0.0409        |
+| 4.1098  | 48500  | 0.0426        |
+| 4.1522  | 49000  | 0.0437        |
+| 4.1946  | 49500  | 0.0422        |
+| 4.2369  | 50000  | 0.0395        |
+| 4.2793  | 50500  | 0.0395        |
+| 4.3217  | 51000  | 0.0425        |
+| 4.3640  | 51500  | 0.0379        |
+| 4.4064  | 52000  | 0.0428        |
+| 4.4488  | 52500  | 0.0412        |
+| 4.4911  | 53000  | 0.0399        |
+| 4.5335  | 53500  | 0.04          |
+| 4.5759  | 54000  | 0.0416        |
+| 4.6183  | 54500  | 0.0351        |
+| 4.6606  | 55000  | 0.037         |
+| 4.7030  | 55500  | 0.0408        |
+| 4.7454  | 56000  | 0.038         |
+| 4.7877  | 56500  | 0.04          |
+| 4.8301  | 57000  | 0.0384        |
+| 4.8725  | 57500  | 0.0372        |
+| 4.9148  | 58000  | 0.0393        |
+| 4.9572  | 58500  | 0.038         |
+| 4.9996  | 59000  | 0.044         |
+| 5.0419  | 59500  | 0.0278        |
+| 5.0843  | 60000  | 0.0257        |
+| 5.1267  | 60500  | 0.0272        |
+| 5.1691  | 61000  | 0.0322        |
+| 5.2114  | 61500  | 0.0234        |
+| 5.2538  | 62000  | 0.029         |
+| 5.2962  | 62500  | 0.0255        |
+| 5.3385  | 63000  | 0.0238        |
+| 5.3809  | 63500  | 0.0287        |
+| 5.4233  | 64000  | 0.0239        |
+| 5.4656  | 64500  | 0.0273        |
+| 5.5080  | 65000  | 0.028         |
+| 5.5504  | 65500  | 0.0283        |
+| 5.5927  | 66000  | 0.027         |
+| 5.6351  | 66500  | 0.0255        |
+| 5.6775  | 67000  | 0.0258        |
+| 5.7199  | 67500  | 0.025         |
+| 5.7622  | 68000  | 0.0251        |
+| 5.8046  | 68500  | 0.0261        |
+| 5.8470  | 69000  | 0.027         |
+| 5.8893  | 69500  | 0.0245        |
+| 5.9317  | 70000  | 0.0266        |
+| 5.9741  | 70500  | 0.0237        |
+| 6.0164  | 71000  | 0.0201        |
+| 6.0588  | 71500  | 0.0166        |
+| 6.1012  | 72000  | 0.0199        |
+| 6.1435  | 72500  | 0.0209        |
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+| 15.4648 | 182500 | 0.0031        |
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+| 15.5919 | 184000 | 0.0029        |
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+| 26.6079 | 314000 | 0.0008        |
+| 26.6503 | 314500 | 0.0012        |
+| 26.6927 | 315000 | 0.0009        |
+| 26.7350 | 315500 | 0.0011        |
+| 26.7774 | 316000 | 0.0012        |
+| 26.8198 | 316500 | 0.0015        |
+| 26.8621 | 317000 | 0.0016        |
+| 26.9045 | 317500 | 0.0015        |
+| 26.9469 | 318000 | 0.0018        |
+| 26.9892 | 318500 | 0.0013        |
+| 27.0316 | 319000 | 0.0019        |
+| 27.0740 | 319500 | 0.0015        |
+| 27.1163 | 320000 | 0.001         |
+| 27.1587 | 320500 | 0.0009        |
+| 27.2011 | 321000 | 0.0007        |
+| 27.2435 | 321500 | 0.0012        |
+| 27.2858 | 322000 | 0.0012        |
+| 27.3282 | 322500 | 0.0011        |
+| 27.3706 | 323000 | 0.0025        |
+| 27.4129 | 323500 | 0.0009        |
+| 27.4553 | 324000 | 0.0015        |
+| 27.4977 | 324500 | 0.0012        |
+| 27.5400 | 325000 | 0.0013        |
+| 27.5824 | 325500 | 0.0013        |
+| 27.6248 | 326000 | 0.0015        |
+| 27.6671 | 326500 | 0.0011        |
+| 27.7095 | 327000 | 0.0022        |
+| 27.7519 | 327500 | 0.001         |
+| 27.7943 | 328000 | 0.0009        |
+| 27.8366 | 328500 | 0.001         |
+| 27.8790 | 329000 | 0.0007        |
+| 27.9214 | 329500 | 0.0013        |
+| 27.9637 | 330000 | 0.0017        |
+| 28.0061 | 330500 | 0.0006        |
+| 28.0485 | 331000 | 0.0011        |
+| 28.0908 | 331500 | 0.0011        |
+| 28.1332 | 332000 | 0.001         |
+| 28.1756 | 332500 | 0.0013        |
+| 28.2179 | 333000 | 0.0012        |
+| 28.2603 | 333500 | 0.001         |
+| 28.3027 | 334000 | 0.001         |
+| 28.3451 | 334500 | 0.0013        |
+| 28.3874 | 335000 | 0.0012        |
+| 28.4298 | 335500 | 0.0015        |
+| 28.4722 | 336000 | 0.0016        |
+| 28.5145 | 336500 | 0.0013        |
+| 28.5569 | 337000 | 0.0011        |
+| 28.5993 | 337500 | 0.0011        |
+| 28.6416 | 338000 | 0.0015        |
+| 28.6840 | 338500 | 0.0014        |
+| 28.7264 | 339000 | 0.0007        |
+| 28.7687 | 339500 | 0.0013        |
+| 28.8111 | 340000 | 0.001         |
+| 28.8535 | 340500 | 0.0009        |
+| 28.8959 | 341000 | 0.0009        |
+| 28.9382 | 341500 | 0.0012        |
+| 28.9806 | 342000 | 0.0011        |
+| 29.0230 | 342500 | 0.0008        |
+| 29.0653 | 343000 | 0.0009        |
+| 29.1077 | 343500 | 0.0009        |
+| 29.1501 | 344000 | 0.0013        |
+| 29.1924 | 344500 | 0.0011        |
+| 29.2348 | 345000 | 0.0009        |
+| 29.2772 | 345500 | 0.0012        |
+| 29.3195 | 346000 | 0.0009        |
+| 29.3619 | 346500 | 0.0008        |
+| 29.4043 | 347000 | 0.0006        |
+| 29.4467 | 347500 | 0.001         |
+| 29.4890 | 348000 | 0.0012        |
+| 29.5314 | 348500 | 0.0013        |
+| 29.5738 | 349000 | 0.001         |
+| 29.6161 | 349500 | 0.0013        |
+| 29.6585 | 350000 | 0.0017        |
+| 29.7009 | 350500 | 0.0009        |
+| 29.7432 | 351000 | 0.0009        |
+| 29.7856 | 351500 | 0.001         |
+| 29.8280 | 352000 | 0.0011        |
+| 29.8703 | 352500 | 0.0008        |
+| 29.9127 | 353000 | 0.0011        |
+| 29.9551 | 353500 | 0.0005        |
+| 29.9975 | 354000 | 0.0017        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.54.1
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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.*
+-->

config-ori.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 293074
+}

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "name": "FineBgem3",
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 293074
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.54.1",
+    "pytorch": "2.8.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 2447487368

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 8192,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d293270e6db315cdef234b823fe7c99518087f047a64cbd6ba52d46d6e050606
+size 25678311