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bug: fix accuracy metric and add test_with_y=True #41

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bug: fix accuracy metric and add test_with_y=True #41

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32 changes: 20 additions & 12 deletions Finetune_GLUE.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -187,10 +187,11 @@
"metadata": {},
"outputs": [],
"source": [
"accuracy_metric = AvgMetric(lambda inp,targ: torch.eq(torch.tensor(inp.argmax(dim=-1)), torch.tensor(targ)).float().mean())\n",
"METRICS = {\n",
" **{ task:[MatthewsCorrCoef()] for task in ['cola']},\n",
" **{ task:[accuracy] for task in ['sst2', 'mnli', 'qnli', 'rte', 'wnli', 'snli','ax']},\n",
" **{ task:[F1Score(), accuracy] for task in ['mrpc', 'qqp']}, \n",
" **{ task:[accuracy_metric] for task in ['sst2', 'mnli', 'qnli', 'rte', 'wnli', 'snli','ax']},\n",
" **{ task:[F1Score(), accuracy_metric] for task in ['mrpc', 'qqp']}, \n",
" **{ task:[PearsonCorrCoef(), SpearmanCorrCoef()] for task in ['stsb']}\n",
"}\n",
"NUM_CLASS = {\n",
Expand Down Expand Up @@ -319,7 +320,7 @@
" glue_dsets[task]['train'] = datasets.concatenate_datasets([glue_dsets[task]['train'], swapped_train])\n",
"\n",
" # Load / Make dataloaders\n",
" hf_dsets = HF_Datasets(glue_dsets[task], hf_toker=hf_tokenizer, n_inp=3,\n",
" hf_dsets = HF_Datasets(glue_dsets[task], hf_toker=hf_tokenizer, n_inp=3, test_with_y=True,\n",
" cols={'inp_ids':TensorText, 'attn_mask':noop, 'token_type_ids':noop, 'label':TensorCategory})\n",
" if c.double_unordered and task in ['mrpc', 'stsb']:\n",
" dl_kwargs = {'train': {'cache_name': f\"double_dl_{c.max_length}_train.json\"}}\n",
Expand All @@ -342,7 +343,7 @@
" glue_dsets['wnli'] = wsc.my_map(partial(wsc_trick_process, hf_toker=hf_tokenizer),\n",
" cache_file_names=\"tricked_{split}.arrow\")\n",
" cols={'prefix':TensorText,'suffix':TensorText,'cands':TensorText,'cand_lens':noop,'label':TensorCategory}\n",
" glue_dls['wnli'] = HF_Datasets(glue_dsets['wnli'], hf_toker=hf_tokenizer, n_inp=4, \n",
" glue_dls['wnli'] = HF_Datasets(glue_dsets['wnli'], hf_toker=hf_tokenizer, n_inp=4, test_with_y=True,\n",
" cols=cols).dataloaders(bs=32, cache_name=\"dl_tricked_{split}.json\")"
]
},
Expand Down Expand Up @@ -718,17 +719,24 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "wnli\nSome weights of the model checkpoint at google/electra-large-discriminator were not used when initializing ElectraForPreTraining: ['electra.embeddings_project.weight', 'electra.embeddings_project.bias']\n- This IS expected if you are initializing ElectraForPreTraining from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n- This IS NOT expected if you are initializing ElectraForPreTraining from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
"output_type": "stream",
"text": [
"wnli\n",
"Some weights of the model checkpoint at google/electra-large-discriminator were not used when initializing ElectraForPreTraining: ['electra.embeddings_project.weight', 'electra.embeddings_project.bias']\n",
"- This IS expected if you are initializing ElectraForPreTraining from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n",
"- This IS NOT expected if you are initializing ElectraForPreTraining from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": ""
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
"metadata": {},
"output_type": "display_data"
}
],
"source": [
Expand Down Expand Up @@ -778,7 +786,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7-final"
"version": "3.10.11"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
Expand Down Expand Up @@ -1277,4 +1285,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
}
}
9 changes: 5 additions & 4 deletions finetune.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,10 +149,11 @@ def after_fit(self):


# %%
accuracy_metric = AvgMetric(lambda inp,targ: torch.eq(torch.tensor(inp.argmax(dim=-1)), torch.tensor(targ)).float().mean())
METRICS = {
**{ task:[MatthewsCorrCoef()] for task in ['cola']},
**{ task:[accuracy] for task in ['sst2', 'mnli', 'qnli', 'rte', 'wnli', 'snli','ax']},
**{ task:[F1Score(), accuracy] for task in ['mrpc', 'qqp']},
**{ task:[accuracy_metric] for task in ['sst2', 'mnli', 'qnli', 'rte', 'wnli', 'snli','ax']},
**{ task:[F1Score(), accuracy_metric] for task in ['mrpc', 'qqp']},
**{ task:[PearsonCorrCoef(), SpearmanCorrCoef()] for task in ['stsb']}
}
NUM_CLASS = {
Expand Down Expand Up @@ -229,7 +230,7 @@ def tokenize_sents_max_len(example, cols, max_len, swap=False):
glue_dsets[task]['train'] = datasets.concatenate_datasets([glue_dsets[task]['train'], swapped_train])

# Load / Make dataloaders
hf_dsets = HF_Datasets(glue_dsets[task], hf_toker=hf_tokenizer, n_inp=3,
hf_dsets = HF_Datasets(glue_dsets[task], hf_toker=hf_tokenizer, n_inp=3, test_with_y=True,
cols={'inp_ids':TensorText, 'attn_mask':noop, 'token_type_ids':noop, 'label':TensorCategory})
if c.double_unordered and task in ['mrpc', 'stsb']:
dl_kwargs = {'train': {'cache_name': f"double_dl_{c.max_length}_train.json"}}
Expand All @@ -245,7 +246,7 @@ def tokenize_sents_max_len(example, cols, max_len, swap=False):
glue_dsets['wnli'] = wsc.my_map(partial(wsc_trick_process, hf_toker=hf_tokenizer),
cache_file_names="tricked_{split}.arrow")
cols={'prefix':TensorText,'suffix':TensorText,'cands':TensorText,'cand_lens':noop,'label':TensorCategory}
glue_dls['wnli'] = HF_Datasets(glue_dsets['wnli'], hf_toker=hf_tokenizer, n_inp=4,
glue_dls['wnli'] = HF_Datasets(glue_dsets['wnli'], hf_toker=hf_tokenizer, n_inp=4, test_with_y=True,
cols=cols).dataloaders(bs=32, cache_name="dl_tricked_{split}.json")

# %% [markdown]
Expand Down