Training completed

README.md

@@ -0,0 +1,50 @@
+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+model-index:
+- name: distilbert-base-uncased-finetuned-emotion
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# distilbert-base-uncased-finetuned-emotion
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

ch3.ipynb

@@ -11,7 +11,12 @@
    "source": [
     "from transformers import AutoTokenizer\n",
     "from bertviz.transformer_neuron_view import BertModel\n",
-    "from bert"
+    "from bertviz.neuron_view import show\n",
+    "\n",
+    "model_ckpt = \"bert_base_uncased\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
+    "model = BertModel.from_pretrained(model_ckpt)\n",
+    "text = \"ti\""
    ]
   }
  ],

model.safetensors

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