Delete fyp

fyp/1_Pooling/config.json

@@ -1,10 +0,0 @@
-{
-  "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
-}

fyp/README.md

@@ -1,440 +0,0 @@
----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:5000
-- loss:CosineSimilarityLoss
-base_model: sentence-transformers/all-MiniLM-L6-v2
-widget:
-- source_sentence: Machine Learning Engineer. We are looking for a Machine Learning
-    Engineer to join our growing team and work on exciting projects.. Energy officer
-    later analysis.; Represent doctor must amount first new.; Standard store herself
-    buy.; Attorney later road drive high could new.; Public near program language..
-    Docker; Azure; Linux; JavaScript; React. SQL; DevOps; Linux; Python; TensorFlow;
-    C#
-  sentences:
-  - Backend Developer. Baby consider fall go. Year role financial firm physical prepare
-    wear financial. Mission training research me mouth home partner.. Article bad
-    style.; On see attorney traditional price reflect tough.; Pay training I.; President
-    the these.; Mouth close debate world nor sport security.. Docker; C#; Flask; NoSQL;
-    Django; Node.js; Cybersecurity. Operation low rich drive.; Receive middle likely.
-  - Cybersecurity Analyst. What light amount modern security receive. Build book street
-    challenge.. Light choice TV.; Beat piece usually day bar cost.; True government
-    sea training door.; Popular who also situation step.. React; TypeScript; NoSQL;
-    Deep Learning; C#; TensorFlow; Django. Hand behavior market religious four might
-    size.; Middle central knowledge fast rise all really.; Become fight argue.; Able
-    hundred force response.
-  - AI Researcher. Rather well administration police seat stand. Red produce yeah
-    site run fly purpose face.. Yourself address might expect his budget bill.; Later
-    top focus guess occur hour.; Have turn quickly help well its.; However research
-    visit.; Commercial building especially capital system each.. Python; Machine Learning;
-    Terraform; Deep Learning; Java; Cybersecurity; Linux. At exactly story letter
-    dream.; Paper experience control author like president girl education.; Education
-    fund hear side mother.; Who then more start various draw along.
-- source_sentence: Full Stack Developer. We are looking for a Full Stack Developer
-    to join our growing team and work on exciting projects.. Threat store center scene
-    country can quite.; Campaign today degree.; Data when risk citizen common.; Current
-    few environment social about page.. Penetration Testing; Java; Node.js; Docker;
-    JavaScript. SQL; AWS; CI/CD; JavaScript; Machine Learning; C#
-  sentences:
-  - Data Scientist. Protect usually song treat front he. Thought style successful
-    suddenly role voice also. When federal hear eat investment.. Campaign environmental
-    none federal.; These poor conference cause capital.; Start rule third ok.; Network
-    age job charge benefit various.; Go almost cost great.. DevOps; React; Cybersecurity;
-    SQL; Linux; Penetration Testing; Docker. Rise it interest try else attorney.;
-    Always everybody fight actually.; Nearly west score go.; Its if less system during.
-  - Frontend Developer. Student knowledge catch trip specific structure activity be.
-    Listen reveal member.. Nature radio serve into.; Speak old side green second travel
-    clear.; Family instead chance entire despite site.; Approach form wonder wrong
-    billion four blood source.. Flask; NoSQL; Azure; Cybersecurity; Node.js; Java;
-    C#. Recently how hot.; Push yourself step word they.; Forward per difficult chance
-    general ten.
-  - AI Researcher. Start ball civil set although. Or environmental place boy because
-    chance.. Within value ahead.; Class democratic candidate arm.; Region represent
-    great note nothing recently low.; Way live according follow walk doctor loss..
-    Azure; TensorFlow; JavaScript; Machine Learning; CI/CD; Kubernetes; Terraform.
-    Here other next over down seem yourself model.; Discover natural generation traditional
-    suddenly management.; Discuss food majority professor.
-- source_sentence: AI Researcher. We are looking for a AI Researcher to join our growing
-    team and work on exciting projects.. Improve hard street ask anyone accept history.;
-    Heavy a through old nothing various.; Fight clearly safe available similar hot.;
-    Movie body accept society heavy six.; Note close bad detail cell.. NoSQL; Azure;
-    Terraform; Flask; Django. Deep Learning; NoSQL; Terraform; Python; CI/CD; Flask
-  sentences:
-  - Frontend Developer. Model purpose most maintain price guess Republican. Manager
-    sure stuff beyond win. Wall type process.. Pattern million task so approach simple.;
-    Letter as tell tough price.; Tree ahead person building report likely see have.;
-    Thought name current hair avoid.. TensorFlow; Azure; DevOps; Machine Learning;
-    C#; React; NoSQL. Possible say son sister.; Nothing good later pressure board
-    stand.; Fly beat green picture stage.; Look sell same off else nature second.
-  - IT Project Manager. South although pass final number pick while. Others run contain
-    book. Bag single mission try true.. Power water determine go step common.; Student
-    people mission author stay.; Cup here father age age food.. React; Flask; Terraform;
-    DevOps; SQL; Docker; NoSQL. Marriage free security his before wear concern.; Future
-    great east use.; Senior plan require bit court often.
-  - Mobile App Developer. Until player time big design ten. Out billion money follow
-    bill so technology. Thousand north particularly difficult. Check social into decade
-    thing minute ahead.. Send memory ago full director although morning.; Relationship
-    sign front actually forget personal cold name.; Near debate notice their.. SQL;
-    AWS; C#; Node.js; Cybersecurity; Machine Learning; React. Produce fly sea.; Middle
-    race risk.; Land foot often action brother dinner.; Sign administration use book
-    section memory tree.
-- source_sentence: Machine Learning Engineer. We are looking for a Machine Learning
-    Engineer to join our growing team and work on exciting projects.. Talk serious
-    or mouth night measure.; Article ahead capital no development.; Do minute chance
-    employee.; Account impact product land never military main show.. Cybersecurity;
-    Terraform; Deep Learning; Python; Linux. Azure; Django; Docker; NoSQL; TypeScript;
-    SQL
-  sentences:
-  - Data Scientist. Next smile gun course six. Performance month bar let expect everything.
-    Whom great heart college people million computer.. Probably we determine information
-    century.; Step heavy animal notice foot police.; True soldier one business car..
-    AWS; JavaScript; Machine Learning; React; CI/CD; Linux; Cybersecurity. Feel field
-    behind matter hair.; Tonight give Mrs organization.
-  - Data Scientist. Among easy indicate statement. Sit natural change strategy start
-    party.. Do hand star modern.; Eat hear for will picture hotel.; Build parent true
-    discover carry involve exactly.. Python; TypeScript; Docker; NoSQL; C#; Linux;
-    SQL. Sound former during way suffer bag want.; History it school look.; Phone
-    into notice piece wait show.
-  - IT Project Manager. Least trade these voice. Choose letter than. Do model effort
-    they not.. Reflect development forward hand.; Investment fall what guess.; Green
-    new instead language board.. Kubernetes; TypeScript; Django; TensorFlow; AWS;
-    C#; Deep Learning. Lay tax group message work statement ago.; Can try heart city.;
-    Positive social increase throw seat share standard.; Front far prepare.
-- source_sentence: Software Engineer. We are looking for a Software Engineer to join
-    our growing team and work on exciting projects.. Suffer class note resource.;
-    Guess really character and right scientist behavior election.; Seat force cultural
-    arm while.; Single maintain from recently.; Not thing wife focus road.. CI/CD;
-    Terraform; DevOps; JavaScript; TypeScript. Docker; Java; Azure; Deep Learning;
-    AWS; Node.js
-  sentences:
-  - Full Stack Developer. Skin direction civil. Toward sure house stay sure if mouth
-    smile.. Range weight foreign.; Safe car at rest speech agency.; Her avoid her
-    heart three behind.; Deal goal send way power.. Azure; NoSQL; TypeScript; Java;
-    Kubernetes; Django; AWS. Environmental entire have charge state require artist.;
-    Among various instead our team.
-  - Software Engineer. Report ahead relate. Among employee that them.. Night continue
-    surface reduce instead education from.; None we forward notice miss wrong few.;
-    Business recently strategy else other recently environment.. Linux; NoSQL; Cybersecurity;
-    Machine Learning; Python; CI/CD; AWS. Social hot pay task commercial.; I throughout
-    participant sense.; Him station low happen available woman parent.; Measure recent
-    rock say city indeed allow value.
-  - Data Scientist. Standard defense clearly project.. Single always argue offer water
-    war.; Meeting certainly leader party heavy mind authority nearly.; Sister certain
-    any itself.; Paper top at area provide.. Cybersecurity; React; C#; TensorFlow;
-    Deep Learning; Penetration Testing; DevOps. Food safe wide key.; Word identify
-    cup life clear.
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
----
-
-# 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). 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:** 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': 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("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'Software Engineer. We are looking for a Software Engineer to join our growing team and work on exciting projects.. Suffer class note resource.; Guess really character and right scientist behavior election.; Seat force cultural arm while.; Single maintain from recently.; Not thing wife focus road.. CI/CD; Terraform; DevOps; JavaScript; TypeScript. Docker; Java; Azure; Deep Learning; AWS; Node.js',
-    'Data Scientist. Standard defense clearly project.. Single always argue offer water war.; Meeting certainly leader party heavy mind authority nearly.; Sister certain any itself.; Paper top at area provide.. Cybersecurity; React; C#; TensorFlow; Deep Learning; Penetration Testing; DevOps. Food safe wide key.; Word identify cup life clear.',
-    'Software Engineer. Report ahead relate. Among employee that them.. Night continue surface reduce instead education from.; None we forward notice miss wrong few.; Business recently strategy else other recently environment.. Linux; NoSQL; Cybersecurity; Machine Learning; Python; CI/CD; AWS. Social hot pay task commercial.; I throughout participant sense.; Him station low happen available woman parent.; Measure recent rock say city indeed allow value.',
-]
-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.*
--->
-
-<!--
-## 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: 5,000 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: 71 tokens</li><li>mean: 88.58 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 96.1 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 0.2</li><li>mean: 0.48</li><li>max: 0.83</li></ul> |
-* Samples:
-  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label             |
-  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
-  | <code>Machine Learning Engineer. We are looking for a Machine Learning Engineer to join our growing team and work on exciting projects.. Good kind capital special human.; Community great goal.; Reach discover wall blood black style after somebody.. Python; TensorFlow; React; CI/CD; AWS. DevOps; Django; Node.js; Azure; CI/CD; Kubernetes</code>                                   | <code>Mobile App Developer. At do American than partner sound. Plan decade industry deep establish wide whole.. All recognize edge southern.; Home hope house develop major there.; Crime push local present thus.. Node.js; Penetration Testing; TensorFlow; Java; C#; Deep Learning; NoSQL. Above people everything.; Eat game left past pull range.; Letter create must including.</code>                                                                     | <code>0.47</code> |
-  | <code>Mobile App Developer. We are looking for a Mobile App Developer to join our growing team and work on exciting projects.. Try hotel where catch reveal help.; Seat nor quality factor movie.; Good image realize respond possible.. Machine Learning; Terraform; JavaScript; Deep Learning; DevOps. Python; JavaScript; Cybersecurity; TensorFlow; Penetration Testing; DevOps</code> | <code>Mobile App Developer. Future large lead tree clear about building. Manage concern stuff shoulder.. Very star necessary military beautiful structure look.; Reveal something church particular instead special.; Than long series central.; Agent sister value.; Teacher production career more safe.. Penetration Testing; Node.js; SQL; TypeScript; Docker; React; Django. Ask song reveal.; Member top power certain pattern.; Trip away success.</code> | <code>0.42</code> |
-  | <code>Frontend Developer. We are looking for a Frontend Developer to join our growing team and work on exciting projects.. Per major government hotel population walk.; Suddenly artist century few research.; Exist to outside son onto member.. CI/CD; Python; Flask; Deep Learning; Java. SQL; Penetration Testing; AWS; Java; Linux; Node.js</code>                                    | <code>Software Engineer. Down which want debate. Situation establish find cold that. Take Republican over set people.. Understand event image suffer.; Kind go alone consumer develop tonight star.; Page radio former imagine evidence pick girl budget.. TypeScript; Cybersecurity; Machine Learning; Azure; NoSQL; SQL; React. End bed stand whatever challenge.; West moment act management can second between.</code>                                       | <code>0.46</code> |
-* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 10
-- `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`: 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`: 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`: 10
-- `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`: 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}
-- `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`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss |
-|:------:|:----:|:-------------:|
-| 1.5974 | 500  | 0.0052        |
-| 3.1949 | 1000 | 0.0039        |
-| 4.7923 | 1500 | 0.0033        |
-| 6.3898 | 2000 | 0.0029        |
-| 7.9872 | 2500 | 0.0026        |
-| 9.5847 | 3000 | 0.0023        |
-
-
-### Framework Versions
-- Python: 3.11.12
-- Sentence Transformers: 3.4.1
-- Transformers: 4.51.3
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.5.2
-- Datasets: 3.5.0
-- Tokenizers: 0.21.1
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-<!--
-## 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.*
--->

fyp/config.json

@@ -1,25 +0,0 @@
-{
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
-  "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "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",
-  "torch_dtype": "float32",
-  "transformers_version": "4.51.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}

fyp/config_sentence_transformers.json

@@ -1,10 +0,0 @@
-{
-  "__version__": {
-    "sentence_transformers": "3.4.1",
-    "transformers": "4.51.3",
-    "pytorch": "2.6.0+cu124"
-  },
-  "prompts": {},
-  "default_prompt_name": null,
-  "similarity_fn_name": "cosine"
-}

fyp/model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1f1da7461cd2e4b865d0f29ef7d24edee9784535b582e6a18f757c6a93c0da76
-size 90864192

fyp/modules.json

@@ -1,20 +0,0 @@
-[
-  {
-    "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"
-  }
-]

fyp/sentence_bert_config.json

@@ -1,4 +0,0 @@
-{
-  "max_seq_length": 256,
-  "do_lower_case": false
-}

fyp/special_tokens_map.json

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

fyp/tokenizer_config.json

@@ -1,65 +0,0 @@
-{
-  "added_tokens_decoder": {
-    "0": {
-      "content": "[PAD]",
-      "lstrip": false,
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