|
--- |
|
datasets: |
|
- MU-NLPC/Calc-gsm8k |
|
- MU-NLPC/Calc-aqua_rat |
|
- MU-NLPC/Calc-math_qa |
|
- MU-NLPC/Calc-ape210k |
|
metrics: |
|
- exact_match |
|
- rouge |
|
license: apache-2.0 |
|
language: |
|
- en |
|
--- |
|
|
|
# Model Card for calcformer-flan-xl |
|
|
|
This model generates reasoning chains over mathematical questions while **using an external tool: Sympy calculator**. |
|
|
|
|
|
## Model Description |
|
|
|
With the idea to offload the symbolic computation from the stochastic language model, |
|
we train this model to utilize a calculator **for all applicable numeric operations**. |
|
This is achieved by training the model to construct calls to the tool's API in this format: |
|
|
|
```html |
|
<gadget id="calculator">100/2</gadget> <output>50</output> |
|
``` |
|
|
|
where `<gadget>` segment triggers a call of the tool, |
|
which is subsequently served by extending model's decoder input context by adding the output of the tool within the `<output>` segment. |
|
|
|
- **Developed by:** Calcformer team |
|
- **Model type:** Autoregressive Encoder-Decoder |
|
- **Language(s):** en |
|
- **Finetuned from:** google/flan-t5-xl |
|
|
|
|
|
## Sources |
|
|
|
- **Repository:** <https://github.com/prompteus/calc-x> |
|
- **Paper:** <https://arxiv.org/abs/2305.15017> |
|
- [**Calcformer model family on HF**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) |
|
- [**Calc-X dataset collection on HF**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) |
|
|
|
|
|
## Usage |
|
|
|
Additionally to conventional generation, using Tool-augmented generation requires |
|
(1) implementation of the tool(s) and |
|
(2) a customization of `generate()` method augmenting input context on-demand with the outputs of the tools. |
|
|
|
You can find these two components implemented in the attached **gadgets/model.py** and **gadgets/gadget.py** in this model's repo |
|
and the project's [home repo](https://github.com/prompteus/calc-x). |
|
|
|
After adding these two scripts to your directory, you can use the model as follows: |
|
|
|
```python |
|
from transformers import T5ForConditionalGeneration, T5Tokenizer |
|
|
|
from gadgets.model import gadget_assisted_model |
|
from gadgets.gadget import Calculator |
|
|
|
GadgetAssistedT5 = gadget_assisted_model(T5ForConditionalGeneration) |
|
model_name = "MU-NLPC/calcformer-flan-xl" |
|
model = GadgetAssistedT5.from_pretrained(model_name) |
|
tokenizer = T5Tokenizer.from_pretrained(model_name) |
|
|
|
model.prepare_for_generate(tokenizer, |
|
enabled_gadgets=[Calculator()], |
|
default_max_tokens=512) |
|
query = """ |
|
The profit from a business transaction is shared among 2 business partners, |
|
Mike and Johnson in the ratio 2:5 respectively. |
|
If Johnson got $2500, how much will Mike have |
|
after spending some of his share on a shirt that costs $200? |
|
""" |
|
|
|
inputs = tokenizer(query, return_tensors="pt") |
|
output_ids = model.generate(**inputs) |
|
tokenizer.decode(output_ids[0], spaces_between_special_tokens=False) |
|
``` |
|
|
|
This returns: |
|
|
|
```html |
|
According to the ratio, for every 5 parts that Johnson gets, Mike gets 2 parts Since Johnson got $2500, |
|
each part is therefore $2500/5 = $<gadget id="calculator">2500/5</gadget><output>500</output> 500 |
|
Mike will get 2*$500 = $<gadget id="calculator">2*500</gadget><output>1_000</output> 1000 |
|
After buying the shirt he will have $1000-$200 = $<gadget id="calculator">1000-200</gadget><output>800</output> 800 left. |
|
Final result is<result>800</result></s> |
|
``` |
|
|
|
## Out-of-Scope Usage |
|
|
|
Note that given the limited scope of the exercises' complexity in the training, this model will not work well for tasks requiring |
|
more complex algebraic operations, including equations, variables and operations outside the scope of (+-*/). |
|
|
|
|
|
## Training |
|
|
|
This model was trained on [Calc-X](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483), a collection of math problem datasets which we converted into CoT with calculator interactions. |
|
We used a standard auto-regressive transformer training, i.e. a conditional next-token prediction with cross-entropy loss. For more detail about data, training or evaluation, see the [Calc-X and Calcformers paper](https://arxiv.org/abs/2305.15017). |
|
|
|
|
|
## Cite |
|
|
|
Please cite the [Calcformers paper](https://arxiv.org/abs/2305.15017) as follows: |
|
|
|
```bibtex |
|
@inproceedings{kadlcik-etal-2023-soft, |
|
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", |
|
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", |
|
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", |
|
month = december, |
|
year = "2023", |
|
address = "Singapore, Singapore", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/2305.15017", |
|
} |
|
``` |
|
|
