From 9655597d1d6d2cc4fb33d3d6e404e94ff6ce4806 Mon Sep 17 00:00:00 2001 From: kadirnar Date: Tue, 5 Aug 2025 16:49:18 +0000 Subject: [PATCH] Move token constants to top and use symbolic names --- nb/Kaggle-Orpheus_(3B)-TTS.ipynb | 129 +++++++++++++++++-------------- nb/Orpheus_(3B)-TTS.ipynb | 129 +++++++++++++++++-------------- 2 files changed, 146 insertions(+), 112 deletions(-) diff --git a/nb/Kaggle-Orpheus_(3B)-TTS.ipynb b/nb/Kaggle-Orpheus_(3B)-TTS.ipynb index c94d2d9b..595a850f 100644 --- a/nb/Kaggle-Orpheus_(3B)-TTS.ipynb +++ b/nb/Kaggle-Orpheus_(3B)-TTS.ipynb @@ -8,7 +8,7 @@ "
\n", "\n", "\n", - " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -46,7 +46,18 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\n!pip install pip3-autoremove\n!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu124\n!pip install unsloth\n!pip install --upgrade transformers==4.53.2 \"huggingface_hub>=0.34.0\" \"datasets>=3.4.1,<4.0.0\"\n\n!pip install snac" + "source": [ + "%%capture\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "\n", + "!pip install pip3-autoremove\n", + "!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu124\n", + "!pip install unsloth\n", + "!pip install --upgrade transformers==4.53.2 \"huggingface_hub>=0.34.0\" \"datasets>=3.4.1,<4.0.0\"\n", + "\n", + "!pip install snac" + ] }, { "cell_type": "markdown", @@ -195,6 +206,29 @@ "dataset = load_dataset(\"MrDragonFox/Elise\", split = \"train\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokeniser_length = 128256\n", + "start_of_text = 128000\n", + "end_of_text = 128009\n", + "\n", + "start_of_speech = tokeniser_length + 1\n", + "end_of_speech = tokeniser_length + 2\n", + "\n", + "start_of_human = tokeniser_length + 3\n", + "end_of_human = tokeniser_length + 4\n", + "\n", + "start_of_ai = tokeniser_length + 5\n", + "end_of_ai = tokeniser_length + 6\n", + "pad_token = tokeniser_length + 7\n", + "\n", + "audio_tokens_start = tokeniser_length + 10" + ] + }, { "cell_type": "code", "execution_count": null, @@ -383,13 +417,13 @@ "\n", " all_codes = []\n", " for i in range(codes[0].shape[1]):\n", - " all_codes.append(codes[0][0][i].item()+128266)\n", - " all_codes.append(codes[1][0][2*i].item()+128266+4096)\n", - " all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))\n", - " all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))\n", - " all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))\n", - " all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))\n", - " all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))\n", + " all_codes.append(codes[0][0][i].item()+audio_tokens_start)\n", + " all_codes.append(codes[1][0][2*i].item()+audio_tokens_start+4096)\n", + " all_codes.append(codes[2][0][4*i].item()+audio_tokens_start+(2*4096))\n", + " all_codes.append(codes[2][0][(4*i)+1].item()+audio_tokens_start+(3*4096))\n", + " all_codes.append(codes[1][0][(2*i)+1].item()+audio_tokens_start+(4*4096))\n", + " all_codes.append(codes[2][0][(4*i)+2].item()+audio_tokens_start+(5*4096))\n", + " all_codes.append(codes[2][0][(4*i)+3].item()+audio_tokens_start+(6*4096))\n", "\n", "\n", " return all_codes\n", @@ -412,23 +446,6 @@ " return example\n", "\n", "dataset = dataset.map(add_codes, remove_columns=[\"audio\"])\n", - "\n", - "tokeniser_length = 128256\n", - "start_of_text = 128000\n", - "end_of_text = 128009\n", - "\n", - "start_of_speech = tokeniser_length + 1\n", - "end_of_speech = tokeniser_length + 2\n", - "\n", - "start_of_human = tokeniser_length + 3\n", - "end_of_human = tokeniser_length + 4\n", - "\n", - "start_of_ai = tokeniser_length + 5\n", - "end_of_ai = tokeniser_length + 6\n", - "pad_token = tokeniser_length + 7\n", - "\n", - "audio_tokens_start = tokeniser_length + 10\n", - "\n", "dataset = dataset.filter(lambda x: x[\"codes_list\"] is not None)\n", "dataset = dataset.filter(lambda x: len(x[\"codes_list\"]) > 0)\n", "\n", @@ -1946,8 +1963,8 @@ " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n", " all_input_ids.append(input_ids)\n", "\n", - "start_token = torch.tensor([[ 128259]], dtype=torch.int64) # Start of human\n", - "end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64) # End of text, End of human\n", + "start_token = torch.tensor([[ start_of_human]], dtype=torch.int64) # Start of human\n", + "end_tokens = torch.tensor([[end_of_text, end_of_human]], dtype=torch.int64) # End of text, End of human\n", "\n", "all_modified_input_ids = []\n", "for input_ids in all_input_ids:\n", @@ -1959,7 +1976,7 @@ "max_length = max([modified_input_ids.shape[1] for modified_input_ids in all_modified_input_ids])\n", "for modified_input_ids in all_modified_input_ids:\n", " padding = max_length - modified_input_ids.shape[1]\n", - " padded_tensor = torch.cat([torch.full((1, padding), 128263, dtype=torch.int64), modified_input_ids], dim=1)\n", + " padded_tensor = torch.cat([torch.full((1, padding), pad_token, dtype=torch.int64), modified_input_ids], dim=1)\n", " attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64), torch.ones((1, modified_input_ids.shape[1]), dtype=torch.int64)], dim=1)\n", " all_padded_tensors.append(padded_tensor)\n", " all_attention_masks.append(attention_mask)\n", @@ -1978,11 +1995,11 @@ " top_p=0.95,\n", " repetition_penalty=1.1,\n", " num_return_sequences=1,\n", - " eos_token_id=128258,\n", + " eos_token_id=end_of_speech,\n", " use_cache = True\n", " )\n", - "token_to_find = 128257\n", - "token_to_remove = 128258\n", + "token_to_find = start_of_speech\n", + "token_to_remove = end_of_speech\n", "\n", "token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)\n", "\n", @@ -2006,7 +2023,7 @@ " row_length = row.size(0)\n", " new_length = (row_length // 7) * 7\n", " trimmed_row = row[:new_length]\n", - " trimmed_row = [t - 128266 for t in trimmed_row]\n", + " trimmed_row = [t - audio_tokens_start for t in trimmed_row]\n", " code_lists.append(trimmed_row)\n", "\n", "\n", @@ -2137,7 +2154,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28/28 [00:01<00:00, 27.83it/s]\n" + "100%|██████████| 28/28 [00:01<00:00, 27.83it/s]\n" ] }, { @@ -2186,7 +2203,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "\n" ] } @@ -2257,9 +2274,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4145e0f0ee3e45eaa45706496c45ad92", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_de11a736d05e463fa25eb5c134a33a42", - "value": "Filter:\u2007100%" + "value": "Filter: 100%" } }, "09a90177afbf47dfa75d09e562f78ef1": { @@ -2370,9 +2387,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1104a11ea07348c09d9d6ea18a7a0c4f", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_c86528d68fb642c6b349e1f72309e9c2", - "value": "Filter:\u2007100%" + "value": "Filter: 100%" } }, "1104a11ea07348c09d9d6ea18a7a0c4f": { @@ -2864,9 +2881,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1c99dbc970074f9788d87847019699d4", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_75dd059f3d13436b9fa15a26c3d56b61", - "value": "\u20071195/1195\u2007[00:56<00:00,\u200722.63\u2007examples/s]" + "value": " 1195/1195 [00:56<00:00, 22.63 examples/s]" } }, "54560d7f982a4af0b680c276cb74e8de": { @@ -3011,9 +3028,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3deacc4ba8a540ef84bdf83d63c5446e", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_111b14c261a4468daa3f68b8dbc79629", - "value": "\u20071195/1195\u2007[00:00<00:00,\u20072586.53\u2007examples/s]" + "value": " 1195/1195 [00:00<00:00, 2586.53 examples/s]" } }, "5c052294982d4da39950473ab7fd832f": { @@ -3032,9 +3049,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_09a90177afbf47dfa75d09e562f78ef1", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_07fb4f7f1aab42f4a0d184ee2c5644ec", - "value": "\u20071195/1195\u2007[00:01<00:00,\u2007988.84\u2007examples/s]" + "value": " 1195/1195 [00:01<00:00, 988.84 examples/s]" } }, "6a92469e5ff545e09ca55265ae57cb59": { @@ -3210,9 +3227,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_54560d7f982a4af0b680c276cb74e8de", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_6a92469e5ff545e09ca55265ae57cb59", - "value": "\u20071195/1195\u2007[00:00<00:00,\u20072636.42\u2007examples/s]" + "value": " 1195/1195 [00:00<00:00, 2636.42 examples/s]" } }, "8937c2575e9f48cb8498dc6554889a77": { @@ -3387,9 +3404,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f04c54b9ac7e4af1b94276c109ed349e", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_bf9ea54f70f6427cbfed6e58a6463583", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "a531278935a54a398db4f296ee88dca3": { @@ -3424,9 +3441,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6f85d840cc744c268da6e7470ffe8a58", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_1db03134e899468bab57cad43f33bb6f", - "value": "\u20071195/1195\u2007[00:01<00:00,\u2007692.61\u2007examples/s]" + "value": " 1195/1195 [00:01<00:00, 692.61 examples/s]" } }, "a8cde83e4c224ff5a519cbe9e850426e": { @@ -3784,9 +3801,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_eb257008691346d3a48ca7a694a6d9fe", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_6ac48a8dfb674c7090c8831841c3bd4d", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "f5199871f0dd40c498abfb9ecec75484": { @@ -3805,9 +3822,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5638ccc92d1342b2a63a3945b957a762", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_3d2aa872ce1a41fd81cdce2bd75a946d", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "fe99495940c24e709e79d4af9991a2df": { @@ -3942,4 +3959,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/nb/Orpheus_(3B)-TTS.ipynb b/nb/Orpheus_(3B)-TTS.ipynb index 549bdd91..48109ed2 100644 --- a/nb/Orpheus_(3B)-TTS.ipynb +++ b/nb/Orpheus_(3B)-TTS.ipynb @@ -8,7 +8,7 @@ "
\n", "\n", "\n", - " Join Discord if you need help + \u2b50 Star us on Github \u2b50\n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n", @@ -46,7 +46,18 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "%%capture\nimport os\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth\nelse:\n # Do this only in Colab notebooks! Otherwise use pip install unsloth\n !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n !pip install --no-deps unsloth\n!pip install snac" + "source": [ + "%%capture\n", + "import os\n", + "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", + " !pip install unsloth\n", + "else:\n", + " # Do this only in Colab notebooks! Otherwise use pip install unsloth\n", + " !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n", + " !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n", + " !pip install --no-deps unsloth\n", + "!pip install snac" + ] }, { "cell_type": "markdown", @@ -195,6 +206,29 @@ "dataset = load_dataset(\"MrDragonFox/Elise\", split = \"train\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokeniser_length = 128256\n", + "start_of_text = 128000\n", + "end_of_text = 128009\n", + "\n", + "start_of_speech = tokeniser_length + 1\n", + "end_of_speech = tokeniser_length + 2\n", + "\n", + "start_of_human = tokeniser_length + 3\n", + "end_of_human = tokeniser_length + 4\n", + "\n", + "start_of_ai = tokeniser_length + 5\n", + "end_of_ai = tokeniser_length + 6\n", + "pad_token = tokeniser_length + 7\n", + "\n", + "audio_tokens_start = tokeniser_length + 10" + ] + }, { "cell_type": "code", "execution_count": null, @@ -383,13 +417,13 @@ "\n", " all_codes = []\n", " for i in range(codes[0].shape[1]):\n", - " all_codes.append(codes[0][0][i].item()+128266)\n", - " all_codes.append(codes[1][0][2*i].item()+128266+4096)\n", - " all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))\n", - " all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))\n", - " all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))\n", - " all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))\n", - " all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))\n", + " all_codes.append(codes[0][0][i].item()+audio_tokens_start)\n", + " all_codes.append(codes[1][0][2*i].item()+audio_tokens_start+4096)\n", + " all_codes.append(codes[2][0][4*i].item()+audio_tokens_start+(2*4096))\n", + " all_codes.append(codes[2][0][(4*i)+1].item()+audio_tokens_start+(3*4096))\n", + " all_codes.append(codes[1][0][(2*i)+1].item()+audio_tokens_start+(4*4096))\n", + " all_codes.append(codes[2][0][(4*i)+2].item()+audio_tokens_start+(5*4096))\n", + " all_codes.append(codes[2][0][(4*i)+3].item()+audio_tokens_start+(6*4096))\n", "\n", "\n", " return all_codes\n", @@ -412,23 +446,6 @@ " return example\n", "\n", "dataset = dataset.map(add_codes, remove_columns=[\"audio\"])\n", - "\n", - "tokeniser_length = 128256\n", - "start_of_text = 128000\n", - "end_of_text = 128009\n", - "\n", - "start_of_speech = tokeniser_length + 1\n", - "end_of_speech = tokeniser_length + 2\n", - "\n", - "start_of_human = tokeniser_length + 3\n", - "end_of_human = tokeniser_length + 4\n", - "\n", - "start_of_ai = tokeniser_length + 5\n", - "end_of_ai = tokeniser_length + 6\n", - "pad_token = tokeniser_length + 7\n", - "\n", - "audio_tokens_start = tokeniser_length + 10\n", - "\n", "dataset = dataset.filter(lambda x: x[\"codes_list\"] is not None)\n", "dataset = dataset.filter(lambda x: len(x[\"codes_list\"]) > 0)\n", "\n", @@ -1946,8 +1963,8 @@ " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n", " all_input_ids.append(input_ids)\n", "\n", - "start_token = torch.tensor([[ 128259]], dtype=torch.int64) # Start of human\n", - "end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64) # End of text, End of human\n", + "start_token = torch.tensor([[ start_of_human]], dtype=torch.int64) # Start of human\n", + "end_tokens = torch.tensor([[end_of_text, end_of_human]], dtype=torch.int64) # End of text, End of human\n", "\n", "all_modified_input_ids = []\n", "for input_ids in all_input_ids:\n", @@ -1959,7 +1976,7 @@ "max_length = max([modified_input_ids.shape[1] for modified_input_ids in all_modified_input_ids])\n", "for modified_input_ids in all_modified_input_ids:\n", " padding = max_length - modified_input_ids.shape[1]\n", - " padded_tensor = torch.cat([torch.full((1, padding), 128263, dtype=torch.int64), modified_input_ids], dim=1)\n", + " padded_tensor = torch.cat([torch.full((1, padding), pad_token, dtype=torch.int64), modified_input_ids], dim=1)\n", " attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64), torch.ones((1, modified_input_ids.shape[1]), dtype=torch.int64)], dim=1)\n", " all_padded_tensors.append(padded_tensor)\n", " all_attention_masks.append(attention_mask)\n", @@ -1978,11 +1995,11 @@ " top_p=0.95,\n", " repetition_penalty=1.1,\n", " num_return_sequences=1,\n", - " eos_token_id=128258,\n", + " eos_token_id=end_of_speech,\n", " use_cache = True\n", " )\n", - "token_to_find = 128257\n", - "token_to_remove = 128258\n", + "token_to_find = start_of_speech\n", + "token_to_remove = end_of_speech\n", "\n", "token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)\n", "\n", @@ -2006,7 +2023,7 @@ " row_length = row.size(0)\n", " new_length = (row_length // 7) * 7\n", " trimmed_row = row[:new_length]\n", - " trimmed_row = [t - 128266 for t in trimmed_row]\n", + " trimmed_row = [t - audio_tokens_start for t in trimmed_row]\n", " code_lists.append(trimmed_row)\n", "\n", "\n", @@ -2137,7 +2154,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28/28 [00:01<00:00, 27.83it/s]\n" + "100%|██████████| 28/28 [00:01<00:00, 27.83it/s]\n" ] }, { @@ -2186,7 +2203,7 @@ " \n", " \n", "\n", - " Join Discord if you need help + \u2b50\ufe0f Star us on Github \u2b50\ufe0f\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "\n" ] } @@ -2257,9 +2274,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4145e0f0ee3e45eaa45706496c45ad92", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_de11a736d05e463fa25eb5c134a33a42", - "value": "Filter:\u2007100%" + "value": "Filter: 100%" } }, "09a90177afbf47dfa75d09e562f78ef1": { @@ -2370,9 +2387,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1104a11ea07348c09d9d6ea18a7a0c4f", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_c86528d68fb642c6b349e1f72309e9c2", - "value": "Filter:\u2007100%" + "value": "Filter: 100%" } }, "1104a11ea07348c09d9d6ea18a7a0c4f": { @@ -2864,9 +2881,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1c99dbc970074f9788d87847019699d4", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_75dd059f3d13436b9fa15a26c3d56b61", - "value": "\u20071195/1195\u2007[00:56<00:00,\u200722.63\u2007examples/s]" + "value": " 1195/1195 [00:56<00:00, 22.63 examples/s]" } }, "54560d7f982a4af0b680c276cb74e8de": { @@ -3011,9 +3028,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3deacc4ba8a540ef84bdf83d63c5446e", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_111b14c261a4468daa3f68b8dbc79629", - "value": "\u20071195/1195\u2007[00:00<00:00,\u20072586.53\u2007examples/s]" + "value": " 1195/1195 [00:00<00:00, 2586.53 examples/s]" } }, "5c052294982d4da39950473ab7fd832f": { @@ -3032,9 +3049,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_09a90177afbf47dfa75d09e562f78ef1", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_07fb4f7f1aab42f4a0d184ee2c5644ec", - "value": "\u20071195/1195\u2007[00:01<00:00,\u2007988.84\u2007examples/s]" + "value": " 1195/1195 [00:01<00:00, 988.84 examples/s]" } }, "6a92469e5ff545e09ca55265ae57cb59": { @@ -3210,9 +3227,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_54560d7f982a4af0b680c276cb74e8de", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_6a92469e5ff545e09ca55265ae57cb59", - "value": "\u20071195/1195\u2007[00:00<00:00,\u20072636.42\u2007examples/s]" + "value": " 1195/1195 [00:00<00:00, 2636.42 examples/s]" } }, "8937c2575e9f48cb8498dc6554889a77": { @@ -3387,9 +3404,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f04c54b9ac7e4af1b94276c109ed349e", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_bf9ea54f70f6427cbfed6e58a6463583", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "a531278935a54a398db4f296ee88dca3": { @@ -3424,9 +3441,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6f85d840cc744c268da6e7470ffe8a58", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_1db03134e899468bab57cad43f33bb6f", - "value": "\u20071195/1195\u2007[00:01<00:00,\u2007692.61\u2007examples/s]" + "value": " 1195/1195 [00:01<00:00, 692.61 examples/s]" } }, "a8cde83e4c224ff5a519cbe9e850426e": { @@ -3784,9 +3801,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_eb257008691346d3a48ca7a694a6d9fe", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_6ac48a8dfb674c7090c8831841c3bd4d", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "f5199871f0dd40c498abfb9ecec75484": { @@ -3805,9 +3822,9 @@ "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5638ccc92d1342b2a63a3945b957a762", - "placeholder": "\u200b", + "placeholder": "​", "style": "IPY_MODEL_3d2aa872ce1a41fd81cdce2bd75a946d", - "value": "Map:\u2007100%" + "value": "Map: 100%" } }, "fe99495940c24e709e79d4af9991a2df": { @@ -3942,4 +3959,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}