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README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+datasets:
+- emnlp2023/Calc-gsm8k
+- emnlp2023/Calc-aqua_rat
+- emnlp2023/Calc-math_qa
+- emnlp2023/Calc-ape210k
+metrics:
+- exact_match
+- rouge
+model-index:
+- name: calc-t5-large
+  results:
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      type: gsm8k
+      name: GSM8K
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 0.420
+    - type: rouge
+      value: 0.627
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      type: aqua_rat
+      name: AQUA-RAT
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 0.06
+    - type: rouge
+      value: 0.323
+license: apache-2.0
+language:
+- en
+---
+
+# Model Card for calc-t5-large
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model generates reasoning chains over mathematical questions while **using an external tool: Sympy calculator**.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+With the idea to offload a symbolic reasoning 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:** Anonymous
+- **Model type:** Autoregressive Encoder-Decoder
+- **Language(s):** en
+- **Finetuned from:** google/calc-t5-large
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/emnlp2023/gadgets
+- **Paper:** Stay tuned!
+
+## 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 **gadget_assisted_model.py** and **gadget.py** in this model's repo 
+and the project's [home repo](https://github.com/emnlp2023/gadgets).
+
+After adding these two scripts to your directory, you can use the model as follows:
+
+```python
+from gadget_assisted_model import GadgetAssistedModel
+from gadget import Calculator
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+
+class GadgetAssistedT5(GadgetAssistedModel, T5ForConditionalGeneration):
+    # GadgetAssistedModel overrides the standard generate() from transformers
+    pass
+
+
+model = GadgetAssistedT5.from_pretrained("emnlp2023/calc-t5-large")
+tokenizer = T5Tokenizer.from_pretrained("emnlp2023/calc-t5-large")
+
+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, Mike got 2/5*$2500 = $<gadget id="calculator">2/5*2500</gadget><output>1_000</output> 1000 
+Mike will have $1000-$200 = $<gadget id="calculator">1000-200</gadget><output>800</output> 800 after buying a shirt. 
+Final result is<result>800</result></s>
+```
+
+### Out-of-Scope Usage
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+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 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. -->
+
+This model was trained on our Calculator-augmented set of [ape210k dataset github](https://github.com/Chenny0808/ape210k), 
+[mathqa HF dataset](https://huggingface.co/datasets/math_qa),
+[gsm8k HF dataset](https://huggingface.co/datasets/gsm8k),
+[aqua_rat](https://huggingface.co/datasets/aqua_rat),
+in a standard auto-regressive setup i.e. for a conditional next-token prediction with teacher-forced prefix.
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+The model was fine-tuned from [google/calc-t5-large](https://huggingface.co/google/calc-t5-large) for TODO steps 
+aiming to maximise exact-match ration on a validation split of the questions from [gsm8k dataset](https://huggingface.co/datasets/gsm8k).
+We fine-tune only TODO of the parameters finding that this circumvents overfitting to relatively small training dataset.
+
+The full training configuration can be identified from the [training script](https://github.com/emnlp2023/gadgets/blob/9185d1fc4b4812321179f8e5cad3e2f2a764f1df/examples/train_gsm8k_flan-t5-slice.py).