SciNLI-4-llama-2-7b

Model Card for Model ID

This model is a fine-tuned version of Llama-2-7b for sentence category classification using the SciNLI dataset. It has been trained to classify scientific sentences into categories like Contrasting, Reasoning, Entailment, and Neutral.

Model Details

Model Description

This model is designed for Natural Language Inference (NLI) on scientific texts. It has been fine-tuned on the SciNLI dataset, which consists of sentence pairs extracted from scholarly papers on NLP and computational linguistics. The model helps in recognizing the semantic relationship between pairs of sentences in scientific texts.

• Developed by: [Firoz Shaik]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [Causal Language Model]
• Language(s) (NLP): [English]
• License: [More Information Needed]
• Finetuned from model [optional]: [Llama-2-7b]

Model Sources [optional]

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Uses

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Direct Use

The model can be directly used for sentence category classification tasks in scientific literature.

Downstream Use [optional]

The model can be integrated into larger NLP pipelines for tasks like scientific text summarization, question answering, and commonsense reasoning.

Out-of-Scope Use

The model is not intended for general-purpose text classification outside the scientific domain. It should not be used for generating text that requires deep factual correctness without further validation.

Bias, Risks, and Limitations

The model may inherit biases present in the SciNLI dataset. It might not perform well on non-scientific texts or texts from domains not represented in the training data.

Recommendations

Users should be aware of the potential biases and limitations. It is recommended to validate the model's predictions, especially for critical applications.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("path_to_model")
model = AutoModelForCausalLM.from_pretrained("path_to_model")

inputs = tokenizer("Your input text", return_tensors="pt")
outputs = model(**inputs)

[More Information Needed]

## Training Details

### Training Data

The dataset can be downloaded from https://shorturl.at/gQKY6.

### Training Procedure

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#### Preprocessing [optional]



#### Training Hyperparameters

Training Hyperparameters
Training regime: bf16 mixed precision
Learning rate: 2e-4
Batch size: 1
Gradient accumulation steps: 4
Warmup steps: 2
Max steps: 20
Optimizer: paged_adamw_8bit
Evaluation strategy: steps
Evaluation steps: 1

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on the SciNLI test set, containing sentence pairs from scientific papers.

#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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