.gitattributes

@@ -6,4 +6,3 @@
 *.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

README.md

@@ -1,54 +0,0 @@
----
-thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
-tags:
-- conversational
-license: mit
----
-
-## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
-
-DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
-The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
-The model is trained on 147M multi-turn dialogue from Reddit discussion thread. 
-
-* Multi-turn generation examples from an interactive environment:
-
-|Role | Response |
-|---------|--------|
-|User | Does money buy happiness? |
-| Bot |  Depends how much money you spend on it .|
-|User | What is the best way to buy happiness ? |
-| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
-|User |This is so difficult ! |
-| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
-
-Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
-
-ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
-
-### How to use
-
-Now we are ready to try out how the model works as a chatting partner!
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-
-# Let's chat for 5 lines
-for step in range(5):
-	# encode the new user input, add the eos_token and return a tensor in Pytorch
-	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
-
-	# append the new user input tokens to the chat history
-	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
-
-	# generated a response while limiting the total chat history to 1000 tokens, 
-	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-	# pretty print last ouput tokens from bot
-	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
-```

config.json

@@ -1,30 +1,19 @@
 {
-  "activation_function": "gelu_new",
+	"architectures": [
-  "architectures": [
+		"GPT2LMHeadModel"
-    "GPT2LMHeadModel"
+	],
-  ],
+	"bos_token_id": 50256,
-  "attn_pdrop": 0.1,
+	"eos_token_ids": [
-  "bos_token_id": 50256,
+		50256
-  "embd_pdrop": 0.1,
+	],
-  "eos_token_id": 50256,
+	"initializer_range": 0.02,
-  "initializer_range": 0.02,
+	"layer_norm_epsilon": 1e-05,
-  "layer_norm_epsilon": 1e-05,
+	"model_type": "gpt2",
-  "model_type": "gpt2",
+	"n_ctx": 1024,
-  "n_ctx": 1024,
+	"n_embd": 1024,
-  "n_embd": 1024,
+	"n_head": 16,
-  "n_head": 16,
+	"n_layer": 24,
-  "n_layer": 24,
+	"n_positions": 1024,
-  "n_positions": 1024,
+	"pad_token_id": 50256,
-  "resid_pdrop": 0.1,
+	"vocab_size": 50257
-  "summary_activation": null,
+}
-  "summary_first_dropout": 0.1,
-  "summary_proj_to_labels": true,
-  "summary_type": "cls_index",
-  "summary_use_proj": true,
-  "task_specific_params": {
-    "conversational": {
-      "max_length": 1000
-    }
-  },
-  "vocab_size": 50257
-}

generation_config.json

@@ -1,6 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 50256,
-  "eos_token_id": 50256,
-  "transformers_version": "4.27.0.dev0"
-}

generation_config_for_conversational.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 50256,
-  "eos_token_id": 50256,
-  "max_length": 1000,
-  "transformers_version": "4.27.0.dev0"
-}

tokenizer_config.json

@@ -1 +0,0 @@
-{"model_max_length": 1024}