add train.py

README.md

@@ -1,11 +1,24 @@
 ---
 license: mit
 datasets:
-- amcoff/skolmat
+  - amcoff/skolmat
 language:
-- sv
+  - sv
 library_name: transformers
 pipeline_tag: text-classification
 widget:
-- text: "Kycklingwok med äggnudlar och sojasås"
+  - text: "Kycklingwok med äggnudlar och sojasås"
+  - text: "Kökets val"
 ---
+
+```python
+from transformers import pipeline
+
+nlp = pipeline(
+    "text-classification",
+    model="amcoff/classify_skolmat",
+    tokenizer="KBLab/bert-base-swedish-cased",
+)
+
+nlp("Kökets val")
+```

train.py

@@ -0,0 +1,67 @@
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    TrainingArguments,
+    Trainer,
+)
+from datasets import load_dataset
+import numpy as np
+import evaluate
+
+dataset = load_dataset("amcoff/skolmat")["train"].train_test_split(test_size=0.1)
+
+id2label = {k: v for k, v in enumerate(dataset["train"].features["label"].names)}
+label2id = {v: k for k, v in id2label.items()}
+
+tokenizer = AutoTokenizer.from_pretrained("KBLab/bert-base-swedish-cased")
+
+max_length = 128
+
+
+def tokenize_function(examples):
+    return tokenizer(
+        examples["meal"], padding="max_length", truncation=True, max_length=max_length
+    )
+
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+small_train_dataset = tokenized_datasets["train"]
+small_eval_dataset = tokenized_datasets["train"]
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "KBLab/bert-base-swedish-cased",
+    num_labels=len(id2label),
+    id2label=id2label,
+    label2id=label2id,
+)
+
+training_args = TrainingArguments(
+    output_dir="trainer",
+    evaluation_strategy="epoch",
+    per_device_train_batch_size=4,
+)
+
+metric = evaluate.load("accuracy")
+
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+
+    predictions = np.argmax(logits, axis=-1)
+
+    return metric.compute(predictions=predictions, references=labels)
+
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=small_train_dataset,
+    eval_dataset=small_eval_dataset,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()
+
+
+trainer.save_model("model")