File size: 2,536 Bytes
0712d03 4d1c666 0712d03 4d1c666 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
license: apache-2.0
datasets:
- race
language:
- en
tags:
- text classification
- multiple-choice
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilbert-uncased-base https://huggingface.co/distilbert-uncased-base
The model was trained using the code from https://github.com/zphang/lrqa. Please refer to and cite the authors.
# Model Details
- **Initial model:** distilbert-uncased-base
- **LR:** 1e-5
- **Epochs:** 3
- **Warmup Ratio:** 0.1 (10%)
- **Batch Size:** 16
- **Max Seq Len:** 512
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** [DistilBERT]
- **Language(s) (NLP):** [English]
- **License:** [Apache-2.0]
- **Finetuned from model [optional]:** [distilbert-uncased-base]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/zphang/lrqa]
- **Dataset:** [https://huggingface.co/datasets/race]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## 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 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. -->
[More Information Needed]
# 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 -->
- **Hardware Type:** A100 - 40GB
- **Hours used:** 4
- **Cloud Provider:** Private
- **Compute Region:** Portugal
- **Carbon Emitted:** 0.18 kgCO2
Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.
|