thearod5
/

pl-bert-siamese-encoder

@@ -1,143 +1,200 @@
 ---
-language: []
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-base_model: microsoft/codebert-base
-widget: []
-pipeline_tag: sentence-similarity
 ---
-# SentenceTransformer based on microsoft/codebert-base
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences & paragraphs to a 768-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:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 768 tokens
-- **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
-```
-TBertEncoder(
-  (encoder_model): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
-  (pooling_model): Pooling({'word_embedding_dimension': 768, '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})
-)
-```
-## 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("thearod5/tbert-siamese-encoder")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-# 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
-### Framework Versions
-- Python: 3.10.11
-- Sentence Transformers: 3.0.0
-- Transformers: 4.41.2
-- PyTorch: 2.2.2
-- Accelerate:
-- Datasets:
-- Tokenizers: 0.19.1
-## Citation
-### BibTeX
-<!--
-## 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.*
--->

 ---
+language: en
+license: mit
+model_name: tbert-siamese-encoder
 ---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** en
+- **License:** mit
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
 ### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
 ## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
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+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
 ## Model Card Contact
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