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README.md

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+---
+title: Gpt2 Chinese Poem
+emoji: 🐨
+colorFrom: blue
+colorTo: pink
+sdk: gradio
+sdk_version: 3.0.26
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+import torch
+
+import gradio as gr
+import torch.nn.functional as F
+
+from transformers import BertTokenizer, GPT2LMHeadModel
+
+tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem")
+model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem")
+model.eval()
+
+def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
+    assert logits.dim() == 1
+    top_k = min(top_k, logits.size(-1))
+    if top_k > 0:
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+    if top_p > 0.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+        indices_to_remove = sorted_indices[sorted_indices_to_remove]
+        logits[indices_to_remove] = filter_value
+    return logits
+
+def generate(input_text):
+    input_ids = [tokenizer.cls_token_id]
+    input_ids.extend( tokenizer.encode(input_text, add_special_tokens=False) )
+    input_ids = torch.tensor( [input_ids] )
+
+    generated = []
+    for _ in range(100):
+        output = model(input_ids)
+
+        next_token_logits = output.logits[0, -1, :]
+        next_token_logits[tokenizer.convert_tokens_to_ids('[UNK]')] = -float('Inf')
+        filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=8, top_p=1)
+        next_token = torch.multinomial( F.softmax(filtered_logits, dim=-1), num_samples=1 )
+        if next_token == tokenizer.sep_token_id:
+            break
+        generated.append( next_token.item() )
+        input_ids = torch.cat((input_ids, next_token.unsqueeze(0)), dim=1)
+
+    return input_text + "".join( tokenizer.convert_ids_to_tokens(generated) )
+
+if __name__ == "__main__":
+    gr.Interface(fn=generate, inputs="text", outputs="text").launch()

requirements.txt

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+torch
+transformers