Add new CrossEncoder model

README.md

@@ -0,0 +1,474 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- cross-encoder
+- generated_from_trainer
+- dataset_size:69699
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/ms-marco-MiniLM-L2-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+metrics:
+- pearson
+- spearman
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: cross-encoder/ms-marco-MiniLM-L2-v2 Finetuned on PV211 HomeWork
+  results:
+  - task:
+      type: cross-encoder-correlation
+      name: Cross Encoder Correlation
+    dataset:
+      name: sts dev
+      type: sts_dev
+    metrics:
+    - type: pearson
+      value: 0.8392209488671921
+      name: Pearson
+    - type: spearman
+      value: 0.729809198818792
+      name: Spearman
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoMSMARCO R100
+      type: NanoMSMARCO_R100
+    metrics:
+    - type: map
+      value: 0.5685
+      name: Map
+    - type: mrr@10
+      value: 0.557
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.6146
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNFCorpus R100
+      type: NanoNFCorpus_R100
+    metrics:
+    - type: map
+      value: 0.3511
+      name: Map
+    - type: mrr@10
+      value: 0.5391
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.3779
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-reranking
+      name: Cross Encoder Reranking
+    dataset:
+      name: NanoNQ R100
+      type: NanoNQ_R100
+    metrics:
+    - type: map
+      value: 0.5917
+      name: Map
+    - type: mrr@10
+      value: 0.6017
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.645
+      name: Ndcg@10
+  - task:
+      type: cross-encoder-nano-beir
+      name: Cross Encoder Nano BEIR
+    dataset:
+      name: NanoBEIR R100 mean
+      type: NanoBEIR_R100_mean
+    metrics:
+    - type: map
+      value: 0.5038
+      name: Map
+    - type: mrr@10
+      value: 0.5659
+      name: Mrr@10
+    - type: ndcg@10
+      value: 0.5459
+      name: Ndcg@10
+---
+
+# cross-encoder/ms-marco-MiniLM-L2-v2 Finetuned on PV211 HomeWork
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L2-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L2-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-L2-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L2-v2) <!-- at revision da2cadf7e0af92ed9f105f41e9857437e07b51f5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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("maennyn/pv211_beir_cqadupstack_crossencoder")
+# Get scores for pairs of texts
+pairs = [
+    ['Do elevator upgrades increase your passive credit earnings, too?', 'I searched for a solution for this problem, but cannot find an answer (or exact replica of the problem) Basically, I set up Multisite on MAMP Pro (Apache port 80, MySQL Port 3306). The set up was smooth, and I created a new site via a subdirectory. The parent theme loads fine. I created a child theme, and it activates (it doesn\'t show a broken message). On the Appearance page it shows the message "This theme requires the parent theme", but underneath the Theme Description. However when I view the front page of the site, the page is blank, and there is no html at all. Would could possibly be the error? I spent a few hours on this already and it\'s not going really well. Code of child theme, only CSS, no functions.php or other php files in the child theme folder.               /*     Theme Name: Confit Child Theme     Author: Automattic     Template: confit     Description: Confit Child Theme 1     Version: 1.0     */          @import url(\'../confit/style.css\');        * Should also mention that the parent functions are not loading either.  Thanks!'],
+    ['Traceback (most recent call last) error appears on terminal', "I've got a binary characteristic and a population $S$ with size $n$ and $P[X] = p$ such that $p$ may be small and $n$ is extremely large. Within this population are subpopulations of various sizes $S_0, S_1, \\dots, S_k \\subset S$. I'd like to be able to select each subpopulation in which $p_i < p$ with some concept of statistical significance. My first inclination is to observe that the standard error on each $p_i$ is $SE_i = \\sqrt{\\frac{\\hat{p_i}(1-\\hat{p_i})}{n}}$ and to compare upper bounds on confidence intervals. $\\{S_i \\; | \\; \\hat{p_i} + 3 \\cdot SE_i < p\\}$, for example. But when $\\hat{p_i} = 0$, then $SE_i = 0$, and this upper bound is 0 even for the smallest subpopulations (like those where $n_i = 1$). Is there any way to express uncertainty in $p_i$ when $\\hat{p_i} = 0$? Maybe through use of $p$ as a prior? **Edit:** It looks like the Jeffreys interval as described in Brown et al. is about what I'm after, though I'm not as-of-yet sure how to apply it."],
+    ['Do I have to install a custom ROM if I root?', 'What is the difference between a battery and a charged capacitor? I can see lot of similarities between capacitor and battery. In both these charges are separated and When not connected in a circuit both can have same Potential difference `V`. The only difference is that battery runs for longer time but a capacitor discharges almost instantaneously. Why this difference? What is the exact cause for the difference in the discharge times?'],
+    ['How to seprate words into two lines in one cell?', 'To me the word "curious" would be something you can be i.e. > I am curious what tomorrow will bring I recently read a text of a student I was supervising which used it as follows > A curious phenomenon is ... With which he meant to say that the phenomenon was peculiar, odd or strange. The only other case I have ever seen this is in the movie title: "The Curious Case of Benjamin Button", but that might be \'artistic freedom\' (since Curious Case has the nice C.. C..). My question is: is the usage of the word "curious" in the meaning of peculiar correct?'],
+    ["Bought game on Steam, but it's not in my Library", "I'm looking to choose open source project hosting site for an F# project using SVN. CodePlex is where the .NET community in general and most F# projects are hosted, but I'm worried TFS + SvnBridge is going to give me headaches. So I'm looking elsewhere and seeking advice here. Or if you think CodePlex is still the best choice in my scenario, I'd like to hear that too. So far, Google Code is looking appealing to me. They have a clean interface and true SVN hosting. But there are close to no F# projects currently hosted (it's not even in their search by programming language list), so I'm wondering if there are any notable downsides besides the lack of community I might encounter. If there is yet another option, I'd like to hear that too. Thanks!"],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'Do elevator upgrades increase your passive credit earnings, too?',
+    [
+        'I searched for a solution for this problem, but cannot find an answer (or exact replica of the problem) Basically, I set up Multisite on MAMP Pro (Apache port 80, MySQL Port 3306). The set up was smooth, and I created a new site via a subdirectory. The parent theme loads fine. I created a child theme, and it activates (it doesn\'t show a broken message). On the Appearance page it shows the message "This theme requires the parent theme", but underneath the Theme Description. However when I view the front page of the site, the page is blank, and there is no html at all. Would could possibly be the error? I spent a few hours on this already and it\'s not going really well. Code of child theme, only CSS, no functions.php or other php files in the child theme folder.               /*     Theme Name: Confit Child Theme     Author: Automattic     Template: confit     Description: Confit Child Theme 1     Version: 1.0     */          @import url(\'../confit/style.css\');        * Should also mention that the parent functions are not loading either.  Thanks!',
+        "I've got a binary characteristic and a population $S$ with size $n$ and $P[X] = p$ such that $p$ may be small and $n$ is extremely large. Within this population are subpopulations of various sizes $S_0, S_1, \\dots, S_k \\subset S$. I'd like to be able to select each subpopulation in which $p_i < p$ with some concept of statistical significance. My first inclination is to observe that the standard error on each $p_i$ is $SE_i = \\sqrt{\\frac{\\hat{p_i}(1-\\hat{p_i})}{n}}$ and to compare upper bounds on confidence intervals. $\\{S_i \\; | \\; \\hat{p_i} + 3 \\cdot SE_i < p\\}$, for example. But when $\\hat{p_i} = 0$, then $SE_i = 0$, and this upper bound is 0 even for the smallest subpopulations (like those where $n_i = 1$). Is there any way to express uncertainty in $p_i$ when $\\hat{p_i} = 0$? Maybe through use of $p$ as a prior? **Edit:** It looks like the Jeffreys interval as described in Brown et al. is about what I'm after, though I'm not as-of-yet sure how to apply it.",
+        'What is the difference between a battery and a charged capacitor? I can see lot of similarities between capacitor and battery. In both these charges are separated and When not connected in a circuit both can have same Potential difference `V`. The only difference is that battery runs for longer time but a capacitor discharges almost instantaneously. Why this difference? What is the exact cause for the difference in the discharge times?',
+        'To me the word "curious" would be something you can be i.e. > I am curious what tomorrow will bring I recently read a text of a student I was supervising which used it as follows > A curious phenomenon is ... With which he meant to say that the phenomenon was peculiar, odd or strange. The only other case I have ever seen this is in the movie title: "The Curious Case of Benjamin Button", but that might be \'artistic freedom\' (since Curious Case has the nice C.. C..). My question is: is the usage of the word "curious" in the meaning of peculiar correct?',
+        "I'm looking to choose open source project hosting site for an F# project using SVN. CodePlex is where the .NET community in general and most F# projects are hosted, but I'm worried TFS + SvnBridge is going to give me headaches. So I'm looking elsewhere and seeking advice here. Or if you think CodePlex is still the best choice in my scenario, I'd like to hear that too. So far, Google Code is looking appealing to me. They have a clean interface and true SVN hosting. But there are close to no F# projects currently hosted (it's not even in their search by programming language list), so I'm wondering if there are any notable downsides besides the lack of community I might encounter. If there is yet another option, I'd like to hear that too. Thanks!",
+    ]
+)
+# [{'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: `sts_dev`
+* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)
+
+| Metric       | Value      |
+|:-------------|:-----------|
+| pearson      | 0.8392     |
+| **spearman** | **0.7298** |
+
+#### Cross Encoder Reranking
+
+* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
+* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
+  ```json
+  {
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | NanoMSMARCO_R100     | NanoNFCorpus_R100    | NanoNQ_R100          |
+|:------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.5685 (+0.0790)     | 0.3511 (+0.0901)     | 0.5917 (+0.1721)     |
+| mrr@10      | 0.5570 (+0.0795)     | 0.5391 (+0.0392)     | 0.6017 (+0.1750)     |
+| **ndcg@10** | **0.6146 (+0.0742)** | **0.3779 (+0.0529)** | **0.6450 (+0.1444)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_R100_mean`
+* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
+  ```json
+  {
+      "dataset_names": [
+          "msmarco",
+          "nfcorpus",
+          "nq"
+      ],
+      "rerank_k": 100,
+      "at_k": 10,
+      "always_rerank_positives": true
+  }
+  ```
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.5038 (+0.1137)     |
+| mrr@10      | 0.5659 (+0.0979)     |
+| **ndcg@10** | **0.5459 (+0.0905)** |
+
+<!--
+## 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: 69,699 training samples
+* Columns: <code>query</code>, <code>document</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                           | document                                                                                           | label                                           |
+  |:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                          | string                                                                                             | int                                             |
+  | details | <ul><li>min: 15 characters</li><li>mean: 49.33 characters</li><li>max: 125 characters</li></ul> | <ul><li>min: 45 characters</li><li>mean: 793.68 characters</li><li>max: 18801 characters</li></ul> | <ul><li>0: ~74.50%</li><li>1: ~25.50%</li></ul> |
+* Samples:
+  | query                                                                         | document                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | label          |
+  |:------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Do elevator upgrades increase your passive credit earnings, too?</code> | <code>I searched for a solution for this problem, but cannot find an answer (or exact replica of the problem) Basically, I set up Multisite on MAMP Pro (Apache port 80, MySQL Port 3306). The set up was smooth, and I created a new site via a subdirectory. The parent theme loads fine. I created a child theme, and it activates (it doesn't show a broken message). On the Appearance page it shows the message "This theme requires the parent theme", but underneath the Theme Description. However when I view the front page of the site, the page is blank, and there is no html at all. Would could possibly be the error? I spent a few hours on this already and it's not going really well. Code of child theme, only CSS, no functions.php or other php files in the child theme folder.               /*     Theme Name: Confit Child Theme     Author: Automattic     Template: confit     Description: Confit Child Theme 1     Version: 1.0     */          @import url('../confit/style.css');        * Should also menti...</code> | <code>0</code> |
+  | <code>Traceback (most recent call last) error appears on terminal</code>      | <code>I've got a binary characteristic and a population $S$ with size $n$ and $P[X] = p$ such that $p$ may be small and $n$ is extremely large. Within this population are subpopulations of various sizes $S_0, S_1, \dots, S_k \subset S$. I'd like to be able to select each subpopulation in which $p_i < p$ with some concept of statistical significance. My first inclination is to observe that the standard error on each $p_i$ is $SE_i = \sqrt{\frac{\hat{p_i}(1-\hat{p_i})}{n}}$ and to compare upper bounds on confidence intervals. $\{S_i \; | \; \hat{p_i} + 3 \cdot SE_i < p\}$, for example. But when $\hat{p_i} = 0$, then $SE_i = 0$, and this upper bound is 0 even for the smallest subpopulations (like those where $n_i = 1$). Is there any way to express uncertainty in $p_i$ when $\hat{p_i} = 0$? Maybe through use of $p$ as a prior? **Edit:** It looks like the Jeffreys interval as described in Brown et al. is about what I'm after, though I'm not as-of-yet sure how to apply it.</code>                             | <code>0</code> |
+  | <code>Do I have to install a custom ROM if I root?</code>                     | <code>What is the difference between a battery and a charged capacitor? I can see lot of similarities between capacitor and battery. In both these charges are separated and When not connected in a circuit both can have same Potential difference `V`. The only difference is that battery runs for longer time but a capacitor discharges almost instantaneously. Why this difference? What is the exact cause for the difference in the discharge times?</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>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`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `save_only_model`: True
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `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`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: True
+- `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}
+- `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`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step     | Training Loss | sts_dev_spearman | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10  | NanoBEIR_R100_mean_ndcg@10 |
+|:-------:|:--------:|:-------------:|:----------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
+| -1      | -1       | -             | 0.5982           | 0.6519 (+0.1115)         | 0.3749 (+0.0498)          | 0.6497 (+0.1490)     | 0.5588 (+0.1035)           |
+| 0.4589  | 1000     | 0.4015        | -                | -                        | -                         | -                    | -                          |
+| 0.9179  | 2000     | 0.191         | -                | -                        | -                         | -                    | -                          |
+| **1.0** | **2179** | **-**         | **0.7298**       | **0.6146 (+0.0742)**     | **0.3779 (+0.0529)**      | **0.6450 (+0.1444)** | **0.5459 (+0.0905)**       |
+| 1.3768  | 3000     | 0.163         | -                | -                        | -                         | -                    | -                          |
+| 1.8357  | 4000     | 0.1524        | -                | -                        | -                         | -                    | -                          |
+| 2.0     | 4358     | -             | 0.7312           | 0.5951 (+0.0547)         | 0.3808 (+0.0557)          | 0.6490 (+0.1484)     | 0.5416 (+0.0863)           |
+| 2.2946  | 5000     | 0.1369        | -                | -                        | -                         | -                    | -                          |
+| 2.7536  | 6000     | 0.1297        | -                | -                        | -                         | -                    | -                          |
+| 3.0     | 6537     | -             | 0.7335           | 0.5994 (+0.0590)         | 0.3743 (+0.0492)          | 0.6500 (+0.1494)     | 0.5412 (+0.0859)           |
+| -1      | -1       | -             | 0.7298           | 0.6146 (+0.0742)         | 0.3779 (+0.0529)          | 0.6450 (+0.1444)     | 0.5459 (+0.0905)           |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.1.0+cu118
+- Accelerate: 1.6.0
+- 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.*
+-->

config.json

@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "4.1.0"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f6273404a5d8df480004d551bc0e1ed6300998761d991faad8d8c739f2446fc
+size 62467588

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 +1,65 @@
+{
+  "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]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}