\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m# Generate translation with target language specified\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtranslated_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforced_bos_token_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlang_code_to_id\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tam\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;31m# Decode output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1107\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1108\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.__class__.__name__} has no attribute {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1109\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mAttributeError\u001b[0m: NllbTokenizerFast has no attribute lang_code_to_id"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "from datasets import load_dataset\n",
- "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer\n",
- "\n",
- "# Load the dataset\n",
- "dataset = load_dataset(\"json\", data_files=\"/content/debug_divas datset .json\")\n",
- "\n",
- "# Load the tokenizer and model\n",
- "model_name = \"facebook/nllb-200-distilled-600M\"\n",
- "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
- "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
- "\n",
- "# Preprocessing function\n",
- "def preprocess_function(examples):\n",
- " inputs = tokenizer(examples[\"input\"], padding=\"max_length\", truncation=True, max_length=128)\n",
- " # Explicitly set the target language before using as_target_tokenizer\n",
- " tokenizer.tgt_lang = \"tam\" # Assuming 'tam' is the target language code\n",
- " with tokenizer.as_target_tokenizer():\n",
- " labels = tokenizer(examples[\"output\"], padding=\"max_length\", truncation=True, max_length=128)\n",
- " inputs[\"labels\"] = labels[\"input_ids\"]\n",
- " return inputs\n",
- "\n",
- "# Apply preprocessing\n",
- "tokenized_datasets = dataset.map(preprocess_function, batched=True, remove_columns=dataset[\"train\"].column_names)\n",
- "\n",
- "# Split into train and test\n",
- "split_datasets = tokenized_datasets[\"train\"].train_test_split(test_size=0.2, seed=42)\n",
- "tokenized_datasets[\"train\"] = split_datasets[\"train\"]\n",
- "tokenized_datasets[\"test\"] = split_datasets[\"test\"]\n",
- "\n",
- "\n",
- "# Training arguments\n",
- "training_args = TrainingArguments(\n",
- " output_dir=\"./fine_tuned_model\",\n",
- " evaluation_strategy=\"epoch\",\n",
- " save_strategy=\"epoch\",\n",
- " learning_rate=2e-5,\n",
- " per_device_train_batch_size=8, # Adjust based on your GPU memory\n",
- " per_device_eval_batch_size=8, # Adjust based on your GPU memory\n",
- " num_train_epochs=3, # Adjust as needed\n",
- " weight_decay=0.01,\n",
- " push_to_hub=True,\n",
- " hub_model_id=\"anitha2520/debug_divas45model\", # Replace with your Hugging Face model ID\n",
- ")\n",
- "\n",
- "# Create Trainer instance\n",
- "trainer = Trainer(\n",
- " model=model,\n",
- " args=training_args,\n",
- " train_dataset=tokenized_datasets[\"train\"],\n",
- " eval_dataset=tokenized_datasets[\"test\"],\n",
- ")\n",
- "\n",
- "# Fine-tune the model\n",
- "trainer.train()\n",
- "\n",
- "# Save the fine-tuned model\n",
- "trainer.save_model(\"./fine_tuned_model\")\n",
- "\n",
- "# Load the fine-tuned model\n",
- "fine_tuned_model = AutoModelForSeq2SeqLM.from_pretrained(\"./fine_tuned_model\")\n",
- "\n",
- "# Prepare input for translation\n",
- "input_text = \"This is a sentence to translate.\"\n",
- "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
- "\n",
- "# Explicitly set the target language\n",
- "tokenizer.tgt_lang = \"tam\" # Assuming 'tam' is the target language code\n",
- "\n",
- "# Generate translation\n",
- "with tokenizer.as_target_tokenizer():\n",
- " translated_tokens = fine_tuned_model.generate(**inputs)\n",
- "\n",
- "# Decode the translated tokens\n",
- "translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)\n",
- "\n",
- "print(translated_text)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 162,
- "referenced_widgets": [
- "42065d9f960740eab5f393ba310eaeeb",
- "aef11a0ad8a64e088e9e48cbd22ca08d",
- "83671982a2dd486c8e0f4e5d4f27fc2d",
- "71c34d318c1d438792506bd10815dbb0",
- "8c19f61265b54b5c978a9c024d576548",
- "d1adbfa1ba234c789be5af4a9adb6cb2",
- "53c3ad0bbb56404a8f0508371a02ea75",
- "ee83fa2e2c8f451c9baaf65bca300544",
- "2d7808b5308446fd9e3aba4c739abd32",
- "f5f905f4cbae4342b7ed921a370c009d",
- "f09f1748de584ac3b950c63ec93e279b"
- ]
- },
- "id": "OVEFHDzjOkoX",
- "outputId": "11cfe45a-6322-48fa-ba35-b5912b766d22"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Map: 0%| | 0/15973 [00:00"
- ],
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- " [ 619/4794 13:31 < 1:31:33, 0.76 it/s, Epoch 0.39/3]\n",
- " \n",
- " \n",
- " \n",
- " | Epoch | \n",
- " Training Loss | \n",
- " Validation Loss | \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " "
- ]
- },
- "metadata": {}
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "from transformers import AutoModelForSeq2SeqLM\n",
- "\n",
- "# Load the fine-tuned model\n",
- "fine_tuned_model = AutoModelForSeq2SeqLM.from_pretrained(\"./fine_tuned_model\")\n",
- "\n",
- "# Prepare input for translation\n",
- "input_text = \"This is a sentence to translate.\"\n",
- "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
- "\n",
- "# Generate translation\n",
- "translated_tokens = fine_tuned_model.generate(**inputs)\n",
- "\n",
- "# Decode the translated tokens\n",
- "translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)\n",
- "\n",
- "print(translated_text)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 772
- },
- "id": "RKj6ebbUPTsh",
- "outputId": "e3349c5e-fb73-4db1-bb5a-20960eefaa1d"
- },
- "execution_count": 46,
- "outputs": [
- {
- "output_type": "error",
- "ename": "ValueError",
- "evalue": "Unrecognized model in ./fine_tuned_model. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: albert, align, altclip, aria, aria_text, audio-spectrogram-transformer, autoformer, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, colpali, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deformable_detr, deit, depth_anything, deta, detr, diffllama, dinat, dinov2, dinov2_with_registers, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, emu3, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, falcon_mamba, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, git, glm, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, granite, granitemoe, graphormer, grounding-dino, groupvit, hiera, hubert, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, jamba, jetmoe, jukebox, kosmos-2, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, llava, llava_next, llava_next_video, llava_onevision, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, mgp-str, mimi, mistral, mixtral, mllama, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, owlv2, owlvit, paligemma, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phi3, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, seggpt, sew, sew-d, siglip, siglip_vision_model, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table-transformer, tapas, textnet, time_series_transformer, timesformer, timm_backbone, timm_wrapper, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vitpose, vitpose_backbone, vits, vivit, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xmod, yolos, yoso, zamba, zoedepth",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m| \u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Load the fine-tuned model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfine_tuned_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForSeq2SeqLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./fine_tuned_model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Prepare input for translation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"quantization_config\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m config, kwargs = AutoConfig.from_pretrained(\n\u001b[0m\u001b[1;32m 527\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0mreturn_unused_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/auto/configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1089\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCONFIG_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1092\u001b[0m \u001b[0;34mf\"Unrecognized model in {pretrained_model_name_or_path}. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[0;34mf\"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: Unrecognized model in ./fine_tuned_model. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: albert, align, altclip, aria, aria_text, audio-spectrogram-transformer, autoformer, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, colpali, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deformable_detr, deit, depth_anything, deta, detr, diffllama, dinat, dinov2, dinov2_with_registers, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, emu3, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, falcon_mamba, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, git, glm, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, granite, granitemoe, graphormer, grounding-dino, groupvit, hiera, hubert, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, jamba, jetmoe, jukebox, kosmos-2, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, llava, ll..."
- ]
- }
- ]
- }
- ]
-}
\ No newline at end of file
| |