Add table question answering

_examples/table_question_answering/main.go

@@ -0,0 +1,33 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+	"os"
+
+	huggingface "github.com/hupe1980/go-huggingface"
+)
+
+func main() {
+	ic := huggingface.NewInferenceClient(os.Getenv("HUGGINGFACEHUB_API_TOKEN"))
+
+	res, err := ic.TableQuestionAnswering(context.Background(), &huggingface.TableQuestionAnsweringRequest{
+		Inputs: huggingface.TableQuestionAnsweringInputs{
+			Query: "How many stars does the transformers repository have?",
+			Table: map[string][]string{
+				"Repository":   {"Transformers", "Datasets", "Tokenizers"},
+				"Stars":        {"36542", "4512", "3934"},
+				"Contributors": {"651", "77", "34"},
+			},
+		},
+	})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	fmt.Println("Answer:", res.Answer)
+	fmt.Println("Coordinates:", res.Coordinates)
+	fmt.Println("Cells:", res.Cells)
+	fmt.Println("Aggregator:", res.Aggregator)
+}

huggingface.go

@@ -12,6 +12,7 @@ import (
 )
 
 var (
+	// recommendedModels stores the recommended models for each task.
 	recommendedModels map[string]string
 )
 
@@ -170,6 +171,31 @@ func (ic *InferenceClient) QuestionAnswering(ctx context.Context, req *QuestionA
 	return questionAnsweringResponse, nil
 }
 
+// TableQuestionAnswering performs table-based question answering using the specified model.
+// It sends a POST request to the Hugging Face inference endpoint with the provided inputs.
+// The response contains the answer or an error if the request fails.
+func (ic *InferenceClient) TableQuestionAnswering(ctx context.Context, req *TableQuestionAnsweringRequest) (*TableQuestionAnsweringResponse, error) {
+	if req.Inputs.Query == "" {
+		return nil, errors.New("query is required")
+	}
+
+	if req.Inputs.Table == nil {
+		return nil, errors.New("table is required")
+	}
+
+	body, err := ic.post(ctx, req.Model, "table-question-answering", req)
+	if err != nil {
+		return nil, err
+	}
+
+	tablequestionAnsweringResponse := TableQuestionAnsweringResponse{}
+	if err := json.Unmarshal(body, &tablequestionAnsweringResponse); err != nil {
+		return nil, err
+	}
+
+	return &tablequestionAnsweringResponse, nil
+}
+
 // FillMask performs masked language modeling using the specified model.
 // It sends a POST request to the Hugging Face inference endpoint with the provided inputs.
 // The response contains the generated text with the masked tokens filled or an error if the request fails.

table_question_answering.go

@@ -0,0 +1,33 @@
+package gohuggingface
+
+// Request structure for table question answering model
+type TableQuestionAnsweringRequest struct {
+	Inputs  TableQuestionAnsweringInputs `json:"inputs"`
+	Options Options                      `json:"options,omitempty"`
+	Model   string                       `json:"-"`
+}
+
+type TableQuestionAnsweringInputs struct {
+	// (Required) The query in plain text that you want to ask the table
+	Query string `json:"query"`
+
+	// (Required) A table of data represented as a dict of list where entries
+	// are headers and the lists are all the values, all lists must
+	// have the same size.
+	Table map[string][]string `json:"table"`
+}
+
+// Response structure for table question answering model
+type TableQuestionAnsweringResponse struct {
+	// The plaintext answer
+	Answer string `json:"answer,omitempty"`
+
+	// A list of coordinates of the cells references in the answer
+	Coordinates [][]int `json:"coordinates,omitempty"`
+
+	// A list of coordinates of the cells contents
+	Cells []string `json:"cells,omitempty"`
+
+	// The aggregator used to get the answer
+	Aggregator string `json:"aggregator,omitempty"`
+}