mikemayuare
/

SELFY-APE-tox21

@@ -1,199 +1,74 @@
 ---
 library_name: transformers
-tags: []
 ---
 # 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. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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):** [More Information Needed]
-- **License:** [More Information Needed]
-- **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:**
-[More Information Needed]
-## 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
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+- tox21
+- SELFIES
+- APE Tokenizer
+- classification
+license: mit
+base_model:
+- mikemayuare/SELFYAPE
 ---
 # Model Card for Model ID
+This model is fine-tuned on the Tox21 dataset and is designed to classify chemical compounds using SELFIES (Self-referencing Embedded Strings) as input representations. It employs the APE (Atom Pair Encoding) tokenizer for tokenizing the input, with the vocabulary stored in the same repository as the model under the file name `tokenizer.json`. The model is intended for sequence classification tasks and should be loaded with the `AutoModelForSequenceClassification` class.
 ## Model Details
 ### Model Description
+This is a 🤗 transformers model fine-tuned on the Tox21 dataset for classifying chemical compounds. It uses the SELFIES molecular representation format as input and tokenizes these inputs using the APE Tokenizer. The vocabulary for the APE tokenizer is stored in the file `tokenizer.json`, located in the same repository as the model.
+- **Developed by:** Miguelangel Leon
+- **Funded by:** This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI:10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
+- **Model type:** Sequence Classification
+- **Language(s) (NLP):** Not applicable (SELFIES molecular representation)
+- **License:** MIT
+- **Finetuned from model [optional]:** mikemayuare/SELFYAPE
+### Model Sources
+- **Paper :** Pending
 ## Uses
 ### Direct Use
+This model can be used directly for classification tasks on chemical compounds. It is specifically designed for toxicity prediction tasks, and the inputs must be formatted as SELFIES.
+### Downstream Use
+This model can be fine-tuned further for other chemical classification tasks if the downstream dataset also uses SELFIES representations.
 ### Out-of-Scope Use
+This model is not suited for tasks that do not involve molecular data or tasks that require natural language processing (NLP).
 ## Bias, Risks, and Limitations
+As this model has been trained on the Tox21 dataset, it may not generalize well to unseen chemical compounds that differ significantly from the training data. Moreover, since this model is designed for molecular data, it is not appropriate for use in non-chemical classification tasks.
 ### Recommendations
+Users should be aware of potential biases stemming from the training data (Tox21). Careful evaluation on the target chemical compounds is recommended to ensure the model's suitability for a given application.
 ## How to Get Started with the Model
+To use the model for classification, it must be loaded with the `AutoModelForSequenceClassification` class from 🤗 transformers. The APE tokenizer is required to process the input data, which should be formatted as SELFIES.
+You can load the APE tokenizer and the model with the following steps:
+```python
+# Install the APETokenizer from the repository
+# !git clone https://github.com/mikemayuare/apetokenizer
+# Load the tokenizer
+from src.apetokenizer.ape_tokenizer import APETokenizer
+tokenizer = APETokenizer()
+tokenizer.load_vocabulary("tokenizer.json")
+# Load the model
+from transformers import AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("mikemayuare/SELFY-APE-tox21")