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Model: Norod78/hewiki-articles-distilGPT2py-il
Source: Original Platform

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: he
+
+thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
+widget:
+- text: "<|startoftext|>החוק השני של מועדון קרב הוא"
+- text: "<|startoftext|>ראש הממשלה בן גוריון"
+- text: "<|startoftext|>למידת מכונה (סרט)"
+- text: "<|startoftext|>מנשה פומפרניקל"
+- text: "<|startoftext|>אי שוויון "
+
+license: mit
+---
+
+
+# hewiki-articles-distilGPT2py-il
+
+## A tiny GPT2 model for generating Hebrew text
+
+A distilGPT2 sized model. <br>
+Training data was hewiki-20200701-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/hewiki/20200701/  <br>
+XML has been converted to plain text using Wikipedia Extractor http://medialab.di.unipi.it/wiki/Wikipedia_Extractor  <br>
+I then added <|startoftext|> and <|endoftext|> markers and deleted empty lines.  <br>
+
+#### How to use
+
+```python
+import torch
+import torch.nn as nn
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+tokenizer = GPT2Tokenizer.from_pretrained("Norod78/hewiki-articles-distilGPT2py-il")
+model = GPT2LMHeadModel.from_pretrained("Norod78/hewiki-articles-distilGPT2py-il").eval()
+
+bos_token = tokenizer.bos_token #Beginning of sentace 
+eos_token = tokenizer.eos_token #End of sentence 
+
+def generate_word(model, tokens_tensor, temperature=1.0):
+  """ 
+  Sample a word given a tensor of tokens of previous words from a model. Given 
+  the words we have, sample a plausible word. Temperature is used for 
+  controlling randomness. If using temperature==0 we simply use a greedy arg max. 
+  Else, we sample from a multinomial distribution using a lower inverse 
+  temperature to allow for more randomness to escape repetitions. 
+  """
+  with torch.no_grad():
+    outputs = model(tokens_tensor)
+    predictions = outputs[0]
+    if temperature>0:
+      # Make the distribution more or less skewed based on the temperature
+      predictions = outputs[0]/temperature
+      # Sample from the distribution
+      softmax = nn.Softmax(dim=0)
+      predicted_index = torch.multinomial(softmax(predictions[0,-1,:]),1).item()
+    # Simply take the arg-max of the distribution
+    else:
+      predicted_index = torch.argmax(predictions[0, -1, :]).item()
+    # Decode the encoding to the corresponding word
+    predicted_text = tokenizer.decode([predicted_index])
+  return predicted_text
+
+def generate_sentence(model, tokenizer, initial_text, temperature=1.0):
+  """ Generate a sentence given some initial text using a model and a tokenizer.
+  Returns the new sentence. """
+        
+  # Encode a text inputs
+  text = ""
+  sentence = text
+
+  # We avoid an infinite loop by setting a maximum range
+  for i in range(0,84):
+    indexed_tokens = tokenizer.encode(initial_text + text)
+      
+    # Convert indexed tokens in a PyTorch tensor
+    tokens_tensor = torch.tensor([indexed_tokens])
+    
+    new_word = generate_word(model, tokens_tensor, temperature=temperature)
+
+    # Here the temperature is slowly decreased with each generated word,
+    # this ensures that the sentence (ending) makes more sense.
+    # We don't decrease to a temperature of 0.0 to leave some randomness in.
+    if temperature<(1-0.008):
+      temperature += 0.008
+    else:
+      temperature = 0.996
+
+    text = text+new_word
+
+    # Stop generating new words when we have reached the end of the line or the poem
+    if eos_token in new_word:
+      # returns new sentence and whether poem is done
+      return (text.replace(eos_token,"").strip(), True)
+    elif '/' in new_word:
+      return (text.strip(), False)
+    elif bos_token in new_word:
+        return (text.replace(bos_token,"").strip(), False)
+      
+  return (text, True)
+
+for output_num in range(1,5):
+  init_text = "בוקר טוב"
+  text = bos_token + init_text
+  for i in range(0,84):
+    sentence = generate_sentence(model, tokenizer, text, temperature=0.9)    
+    text = init_text + sentence[0]
+    print(text)
+    if (sentence[1] == True):
+      break   
+```

added_tokens.json

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+{"<|startoftext|>": 50257, "<|endoftext|>": 50258}

config.json

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+{
+  "_num_labels": 1,
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_layer": 6,
+  "n_positions": 1024,
+  "resid_pdrop": 0.1,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "vocab_size": 50259
+}

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special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>"
+}

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tokenizer_config.json

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+{"do_lower_case": false, "max_len": 1024, "bos_token": "<|startoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>", "special_tokens_map_file": "hewiki_title_abstract_distilGPT2/special_tokens_map.json", "full_tokenizer_file": null}