Subiendo modelo inicial

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6552
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-small-en-v1.5
+widget:
+- source_sentence: What does the RAT (Radio Access Technology) Agnostic Control Functions
+    (RACFs) interface with?
+  sentences:
+  - TLS/SSL is an encryption protocol that creates a secure data channel on an insecure
+    network to make client/server applications secure.
+  - TLS/SSL ensures that data transferred between client and server applications cannot
+    be misused or read, and detects if data have been amended during transmission.
+  - The RAT Agnostic Control Functions (RACFs) interface with the Flow Controller
+    and RAT Specific Control Functions (RSCFs).
+- source_sentence: What is an ultra-dense network (UDN)?
+  sentences:
+  - The Markov decision process approach models the delay-aware cross-layer optimization
+    problem as an infinite horizon average cost MDP.
+  - DNS (Domain Name System) is a system that translates human-friendly domain names
+    into IP addresses used by computers to locate servers on the internet.
+  - The document defines an ultra-dense network (UDN) as a network with the spatial
+    density of cells much larger than that of active users.
+- source_sentence: What is the main benefit of storage coding for video files?
+  sentences:
+  - In the context of deep learning, GPU stands for Graphics Processing Unit, which
+    enables parallel computing for faster inference.
+  - Storage coding allows the recovery of original video files even in the case of
+    multiple failures.
+  - For DMG STAs, the TXOP holder may transmit a frame using a modulation class other
+    than the DMG Control modulation class at the start of the TXOP if the time elapsed
+    since the last frame received from the TXOP responder is shorter than the Heartbeat
+    Elapsed Time value computed using the Heartbeat Elapsed Indication field within
+    the TXOP responder's DMG Capabilities element.
+- source_sentence: What is the primary parameter used to measure the severity of narrowband
+    fading in AG (air to ground) propagation channels?
+  sentences:
+  - The main difference between the Wyner's wiretap coding scheme and the coding scheme
+    used in FB-CSs is that Wyner's scheme uses codeword rate and rate redundancy,
+    while the FB-CSs scheme uses coding rate directly.
+  - The RA field is the individual address of the STA that is the immediate intended
+    receiver of the frame.
+  - The severity of narrowband fading in AG propagation channels is measured using
+    the Ricean K-factor, which is the ratio of dominant channel component power to
+    the power in the sum of all other received components.
+- source_sentence: What is one way to enhance the robustness of terahertz in real-time
+    communication?
+  sentences:
+  - Handover refers to the process of changing the serving cell of a UE in RRC_CONNECTED.
+  - Enhancing beam tracking, resource allocation, and user association can improve
+    the robustness of terahertz in real-time communication.
+  - NG-PON has a one-way latency of 2.5 μs, which is the lowest among all the fronthaul
+    technologies.
+datasets:
+- dinho1597/Telecom-QA-MultipleChoice
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_recall@1
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: telecom ir eval
+      type: telecom-ir-eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9482044198895028
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9910220994475138
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9924033149171271
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9951657458563536
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9482044198895028
+      name: Cosine Precision@1
+    - type: cosine_recall@1
+      value: 0.9482044198895028
+      name: Cosine Recall@1
+    - type: cosine_ndcg@10
+      value: 0.9753237736211358
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9685674822415152
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9688408433724855
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice)
+<!-- - **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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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 = [
+    'What is one way to enhance the robustness of terahertz in real-time communication?',
+    'Enhancing beam tracking, resource allocation, and user association can improve the robustness of terahertz in real-time communication.',
+    'Handover refers to the process of changing the serving cell of a UE in RRC_CONNECTED.',
+]
+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
+
+#### Information Retrieval
+
+* Dataset: `telecom-ir-eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy@1  | 0.9482     |
+| cosine_accuracy@3  | 0.991      |
+| cosine_accuracy@5  | 0.9924     |
+| cosine_accuracy@10 | 0.9952     |
+| cosine_precision@1 | 0.9482     |
+| cosine_recall@1    | 0.9482     |
+| **cosine_ndcg@10** | **0.9753** |
+| cosine_mrr@10      | 0.9686     |
+| cosine_map@100     | 0.9688     |
+
+<!--
+## 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
+
+#### telecom-qa-multiple_choice
+
+* Dataset: [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) at [73aebbb](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice/tree/73aebbb16651212e4b1947ac0d64fc80a6bc9398)
+* Size: 6,552 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: 4 tokens</li><li>mean: 18.54 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 29.33 tokens</li><li>max: 100 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                         | positive                                                                                                                                                                                                                                                                                                                                                         |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the two mechanisms in a CDMA (Code Division Multiple Access) system that reduce the chances of significant interference?</code> | <code>In a CDMA system, power control and soft handoff mechanisms are used to reduce the chances of significant interference. Power control ensures that there is no significant intra-cell interference, and soft handoff selects the base station with the best reception to control the user's power, reducing the chance of out-of-cell interference.</code> |
+  | <code>What type of traffic is LPWAN (Low-Power Wide Area Network) suitable for?</code>                                                         | <code>LPWANs are suitable for sporadic and intermittent transmissions of very small packets.</code>                                                                                                                                                                                                                                                              |
+  | <code>What is the definition of an Authenticator in IEEE Std 802.11-2020?</code>                                                               | <code>An Authenticator is an entity at one end of a point-to-point LAN segment that facilitates authentication of the entity attached to the other end of that link.</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
+
+#### telecom-qa-multiple_choice
+
+* Dataset: [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) at [73aebbb](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice/tree/73aebbb16651212e4b1947ac0d64fc80a6bc9398)
+* Size: 6,552 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: 4 tokens</li><li>mean: 18.83 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 28.7 tokens</li><li>max: 99 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                          | positive                                                                                                                                                                                                                          |
+  |:----------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How is the continuous-time impulse response of the end-to-end system computed?</code>                     | <code>The continuous-time impulse response of the end-to-end system is computed as the convolution of the impulse responses of the RIS paths and the propagation channels.</code>                                                 |
+  | <code>What is lattice staggering in a multicarrier scheme?</code>                                               | <code>Lattice staggering is a methodology that generates inherent orthogonality between the points in the lattice for the real domain by using different prototype filters for the real and imaginary parts of the scheme.</code> |
+  | <code>What are the benefits of using Mobile Edge Computing (MEC) for computation-intensive applications?</code> | <code>MEC enables applications to be split into small tasks with some of the tasks performed at the local or regional clouds, reducing delay and backhaul usage.</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`: 128
+- `per_device_eval_batch_size`: 128
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine_with_restarts
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: cosine_with_restarts
+- `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`: 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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `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 | telecom-ir-eval_cosine_ndcg@10 |
+|:----------:|:------:|:-------------:|:---------------:|:------------------------------:|
+| 0.3659     | 15     | 0.5651        | 0.1124          | 0.9593                         |
+| 0.7317     | 30     | 0.144         | 0.0674          | 0.9713                         |
+| 1.0976     | 45     | 0.0783        | 0.0605          | 0.9730                         |
+| **1.4634** | **60** | **0.0633**    | **0.0607**      | **0.9753**                     |
+| 1.8293     | 75     | 0.0469        | 0.0570          | 0.9749                         |
+| 2.1951     | 90     | 0.0297        | 0.0568          | 0.9755                         |
+| 2.5610     | 105    | 0.0287        | 0.0583          | 0.9749                         |
+| 2.9268     | 120    | 0.0266        | 0.0594          | 0.9747                         |
+| 3.2927     | 135    | 0.0196        | 0.0604          | 0.9738                         |
+| 3.6585     | 150    | 0.0191        | 0.0609          | 0.9742                         |
+| 4.0244     | 165    | 0.0167        | 0.0608          | 0.9749                         |
+| 4.3902     | 180    | 0.0165        | 0.0611          | 0.9746                         |
+| 4.7561     | 195    | 0.016         | 0.0611          | 0.9746                         |
+| 5.0        | 205    | -             | -               | 0.9753                         |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- 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.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3a89325a91a1814f74f9f16c67b9e60d4e246ff9577a9858772f23410241b1d
+size 133462128

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": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "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

@@ -0,0 +1,58 @@
+{
+  "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]"
+}