| | --- |
| | language: en |
| | license: mit |
| | model_id: Statement_Equivalence |
| | developers: Matt Stammers |
| | model_type: BERT-Base-Uncased |
| | model_summary: This model Compares the similarity of two text objects. It is the first |
| | BERT model I have fine tuned so there may be bugs. The model labels should read |
| | equivalent/not-equivalent but despite mapping the id2label variables they are presently |
| | still displaying as label0/1 in the inference module. I may come back and fix this |
| | at a later date. |
| | shared_by: Matt Stammers |
| | finetuned_from: Glue |
| | repo: https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you |
| | paper: N/A |
| | demo: N/A |
| | direct_use: Test it out here |
| | downstream_use: This is a standalone app |
| | out_of_scope_use: The model will not work with any very complex sentences or to compare |
| | more than 3 statements |
| | bias_risks_limitations: Biases inherent in Glue also apply here |
| | bias_recommendations: Do not be surprised if unusual results are obtained |
| | get_started_code: "\n ``` python \n # Use a pipeline as a high-level helper\n\ |
| | \ from transformers import pipeline\n\n pipe = pipeline(\"text-classification\"\ |
| | , model=\"MattStammers/Statement_Equivalence\")\n # Load model directly\n \ |
| | \ from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\ |
| | \n tokenizer = AutoTokenizer.from_pretrained(\"MattStammers/Statement_Equivalence\"\ |
| | )\n model = AutoModelForSequenceClassification.from_pretrained(\"MattStammers/Statement_Equivalence\"\ |
| | )\n ```\n " |
| | training_data: 'See Glue Dataset: https://huggingface.co/datasets/glue' |
| | preprocessing: Sentence Pairs to analyse similarity |
| | training_regime: User Defined |
| | speeds_sizes_times: Not Relevant |
| | testing_data: 'MRCP. Link: https://huggingface.co/datasets/SetFit/mrpc' |
| | testing_factors: N/A |
| | testing_metrics: N/A |
| | results: See evaluation results. |
| | results_summary: See Over |
| | model_examination: Model should be interpreted with user discretion. |
| | model_specs: Bert fine-tuned |
| | compute_infrastructure: requires less than 4GB of GPU to run quickly |
| | hardware: T600 |
| | hours_used: '0.1' |
| | cloud_provider: N/A |
| | cloud_region: N/A |
| | co2_emitted: <1 |
| | software: Python, pytorch with transformers |
| | citation_bibtex: N/A |
| | citation_apa: N/A |
| | glossary: N/A |
| | more_information: Can be made available on request |
| | model_card_authors: Matt Stammers |
| | model_card_contact: Matt Stammers |
| | model-index: |
| | - name: statement |
| | results: |
| | - task: |
| | type: text-classification |
| | dataset: |
| | name: MRCP |
| | type: mrcp |
| | metrics: |
| | - type: accuracy |
| | value: 0.8480392156862745 |
| | - type: F1-Score |
| | value: 0.8945578231292517 |
| | --- |
| | |
| | # Model Card for Statement_Equivalence |
| | |
| | <!-- Provide a quick summary of what the model is/does. --> |
| | |
| | This model Compares the similarity of two text objects. It is the first BERT model I have fine tuned so there may be bugs. The model labels should read equivalent/not-equivalent but despite mapping the id2label variables they are presently still displaying as label0/1 in the inference module. I may come back and fix this at a later date. |
| | |
| | ## Model Details |
| | |
| | ### Model Description |
| | |
| | <!-- Provide a longer summary of what this model is. --> |
| | |
| | |
| | |
| | - **Developed by:** Matt Stammers |
| | - **Shared by [optional]:** Matt Stammers |
| | - **Model type:** BERT-Base-Uncased |
| | - **Language(s) (NLP):** en |
| | - **License:** mit |
| | - **Finetuned from model [optional]:** Glue |
| | |
| | ### Model Sources [optional] |
| | |
| | <!-- Provide the basic links for the model. --> |
| | |
| | - **Repository:** https://huggingface.co/MattStammers/Statement_Equivalence?text=I+like+you.+I+love+you |
| | - **Paper [optional]:** N/A |
| | - **Demo [optional]:** N/A |
| |
|
| | ## 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. --> |
| |
|
| | Test it out here |
| |
|
| | ### Downstream Use [optional] |
| |
|
| | <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
| |
|
| | This is a standalone app |
| |
|
| | ### Out-of-Scope Use |
| |
|
| | <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
| |
|
| | The model will not work with any very complex sentences or to compare more than 3 statements |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
| |
|
| | Biases inherent in Glue also apply here |
| |
|
| | ### Recommendations |
| |
|
| | <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
| |
|
| | Do not be surprised if unusual results are obtained |
| |
|
| | ## How to Get Started with the Model |
| |
|
| | Use the code below to get started with the model. |
| |
|
| |
|
| | ``` python |
| | # Use a pipeline as a high-level helper |
| | from transformers import pipeline |
| | |
| | pipe = pipeline("text-classification", model="MattStammers/Statement_Equivalence") |
| | # Load model directly |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("MattStammers/Statement_Equivalence") |
| | model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Statement_Equivalence") |
| | ``` |
| | |
| | |
| | ## Training Details |
| |
|
| | ### Training Data |
| |
|
| | <!-- This should link to a Data 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. --> |
| |
|
| | See Glue Dataset: https://huggingface.co/datasets/glue |
| |
|
| | ### 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] |
| |
|
| | Sentence Pairs to analyse similarity |
| |
|
| |
|
| | #### Training Hyperparameters |
| |
|
| | - **Training regime:** User Defined <!--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. --> |
| |
|
| | Not Relevant |
| |
|
| | ## Evaluation |
| |
|
| | <!-- This section describes the evaluation protocols and provides the results. --> |
| |
|
| | ### Testing Data, Factors & Metrics |
| |
|
| | #### Testing Data |
| |
|
| | <!-- This should link to a Data Card if possible. --> |
| |
|
| | MRCP. Link: https://huggingface.co/datasets/SetFit/mrpc |
| |
|
| | #### Factors |
| |
|
| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
| |
|
| | N/A |
| |
|
| | #### Metrics |
| |
|
| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| |
|
| | N/A |
| |
|
| | ### Results |
| |
|
| | See evaluation results. |
| |
|
| | #### Summary |
| |
|
| | See Over |
| |
|
| | ## Model Examination [optional] |
| |
|
| | <!-- Relevant interpretability work for the model goes here --> |
| |
|
| | Model should be interpreted with user discretion. |
| |
|
| | ## 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:** T600 |
| | - **Hours used:** 0.1 |
| | - **Cloud Provider:** N/A |
| | - **Compute Region:** N/A |
| | - **Carbon Emitted:** <1 |
| |
|
| | ## Technical Specifications [optional] |
| |
|
| | ### Model Architecture and Objective |
| |
|
| | Bert fine-tuned |
| |
|
| | ### Compute Infrastructure |
| |
|
| | requires less than 4GB of GPU to run quickly |
| |
|
| | #### Hardware |
| |
|
| | T600 |
| |
|
| | #### Software |
| |
|
| | Python, pytorch with transformers |
| |
|
| | ## 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:** |
| |
|
| | N/A |
| |
|
| | **APA:** |
| |
|
| | N/A |
| |
|
| | ## Glossary [optional] |
| |
|
| | <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
| |
|
| | N/A |
| |
|
| | ## More Information [optional] |
| |
|
| | Can be made available on request |
| |
|
| | ## Model Card Authors [optional] |
| |
|
| | Matt Stammers |
| |
|
| | ## Model Card Contact |
| |
|
| | Matt Stammers |
| |
|
| |
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| |
|
