Commit be02176a authored by Morgan Funtowicz's avatar Morgan Funtowicz
Browse files

Fixing sentiment pipeline in 03-pipelines notebook.


Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>
parent 8a2d9bc9
......@@ -67,27 +67,16 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 6,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%% code \n"
}
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "from __future__ imports must occur at the beginning of the file (<ipython-input-29-c3a037bd4c55>, line 5)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-29-c3a037bd4c55>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m from transformers import pipeline\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m from __future__ imports must occur at the beginning of the file\n"
]
}
],
"outputs": [],
"source": [
"import numpy as np\n",
"from __future__ import print_function\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"from transformers import pipeline"
]
......@@ -105,7 +94,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -115,40 +104,35 @@
"outputs": [
{
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "6aeccfdf51994149bdd1f3d3533e380f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
]
"version_minor": 0,
"model_id": "c9db53f30b9446c0af03268633a966c0"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
],
"output_type": "stream"
},
{
"data": {
"text/plain": [
"[{'label': 'POSITIVE', 'score': 0.800251},\n",
" {'label': 'NEGATIVE', 'score': 1.2489903}]"
]
"text/plain": "[{'label': 'POSITIVE', 'score': 0.9997656}]"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result",
"execution_count": 8
}
],
"source": [
"nlp_sentence_classif = pipeline('sentiment-analysis')\n",
"nlp_sentence_classif(['Such a nice weather outside !', 'This movie was kind of boring.'])"
"nlp_sentence_classif('Such a nice weather outside !')"
]
},
{
......@@ -164,7 +148,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 9,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -174,40 +158,30 @@
"outputs": [
{
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5549c53c27346a899af553c977f00bc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
]
"version_minor": 0,
"model_id": "1e300789e22644f1aed66a5ed60e75c4"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
],
"output_type": "stream"
},
{
"data": {
"text/plain": [
"[{'word': 'Hu', 'score': 0.9970937967300415, 'entity': 'I-ORG'},\n",
" {'word': '##gging', 'score': 0.9345750212669373, 'entity': 'I-ORG'},\n",
" {'word': 'Face', 'score': 0.9787060022354126, 'entity': 'I-ORG'},\n",
" {'word': 'French', 'score': 0.9981995820999146, 'entity': 'I-MISC'},\n",
" {'word': 'New', 'score': 0.9983047246932983, 'entity': 'I-LOC'},\n",
" {'word': '-', 'score': 0.8913455009460449, 'entity': 'I-LOC'},\n",
" {'word': 'York', 'score': 0.9979523420333862, 'entity': 'I-LOC'}]"
]
"text/plain": "[{'word': 'Hu', 'score': 0.9970937967300415, 'entity': 'I-ORG'},\n {'word': '##gging', 'score': 0.9345750212669373, 'entity': 'I-ORG'},\n {'word': 'Face', 'score': 0.9787060022354126, 'entity': 'I-ORG'},\n {'word': 'French', 'score': 0.9981995820999146, 'entity': 'I-MISC'},\n {'word': 'New', 'score': 0.9983047246932983, 'entity': 'I-LOC'},\n {'word': '-', 'score': 0.8913455009460449, 'entity': 'I-LOC'},\n {'word': 'York', 'score': 0.9979523420333862, 'entity': 'I-LOC'}]"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result",
"execution_count": 9
}
],
"source": [
......@@ -224,7 +198,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 10,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -234,42 +208,38 @@
"outputs": [
{
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "6e56a8edcef44ec2ae838711ecd22d3a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
]
"version_minor": 0,
"model_id": "82aca58f1ea24b4cb37f16402e8a5923"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
],
"output_type": "stream"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 53.05it/s]\n",
"add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 2673.23it/s]\n"
]
"convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 225.51it/s]\n",
"add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 2158.67it/s]\n"
],
"output_type": "stream"
},
{
"data": {
"text/plain": [
"{'score': 0.9632966867654424, 'start': 42, 'end': 50, 'answer': 'New-York.'}"
]
"text/plain": "{'score': 0.9632966867654424, 'start': 42, 'end': 50, 'answer': 'New-York.'}"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result",
"execution_count": 10
}
],
"source": [
......@@ -286,7 +256,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 11,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -296,48 +266,30 @@
"outputs": [
{
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "1930695ea2d24ca98c6d7c13842d377f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
]
"version_minor": 0,
"model_id": "49df2227b4fa4eb28dcdcfc3d9261d0f"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
],
"output_type": "stream"
},
{
"data": {
"text/plain": [
"[{'sequence': '<s> Hugging Face is a French company based in Paris</s>',\n",
" 'score': 0.25288480520248413,\n",
" 'token': 2201},\n",
" {'sequence': '<s> Hugging Face is a French company based in Lyon</s>',\n",
" 'score': 0.07639515399932861,\n",
" 'token': 12790},\n",
" {'sequence': '<s> Hugging Face is a French company based in Brussels</s>',\n",
" 'score': 0.055500105023384094,\n",
" 'token': 6497},\n",
" {'sequence': '<s> Hugging Face is a French company based in Geneva</s>',\n",
" 'score': 0.04264815151691437,\n",
" 'token': 11559},\n",
" {'sequence': '<s> Hugging Face is a French company based in France</s>',\n",
" 'score': 0.03868963569402695,\n",
" 'token': 1470}]"
]
"text/plain": "[{'sequence': '<s> Hugging Face is a French company based in Paris</s>',\n 'score': 0.23106691241264343,\n 'token': 2201},\n {'sequence': '<s> Hugging Face is a French company based in Lyon</s>',\n 'score': 0.0819825753569603,\n 'token': 12790},\n {'sequence': '<s> Hugging Face is a French company based in Geneva</s>',\n 'score': 0.04769463092088699,\n 'token': 11559},\n {'sequence': '<s> Hugging Face is a French company based in Brussels</s>',\n 'score': 0.047622501850128174,\n 'token': 6497},\n {'sequence': '<s> Hugging Face is a French company based in France</s>',\n 'score': 0.04130595177412033,\n 'token': 1470}]"
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result",
"execution_count": 11
}
],
"source": [
......@@ -354,7 +306,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 12,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -364,34 +316,30 @@
"outputs": [
{
"data": {
"text/plain": "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "92fa4d67290f49a3943dc0abd7529892",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
]
"version_minor": 0,
"model_id": "2af4cfb19e3243dda014d0f56b48f4b2"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
],
"output_type": "stream"
},
{
"data": {
"text/plain": [
"(1, 12, 768)"
]
"text/plain": "(1, 12, 768)"
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result",
"execution_count": 12
}
],
"source": [
......@@ -417,7 +365,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 13,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -427,41 +375,27 @@
"outputs": [
{
"data": {
"text/plain": "Dropdown(description='Task:', index=1, options=('sentiment-analysis', 'ner', 'fill_mask'), value='ner')",
"application/vnd.jupyter.widget-view+json": {
"model_id": "261ae9fa30e84d1d84a3b0d9682ac477",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Task:', index=1, options=('sentiment-analysis', 'ner', 'fill_mask'), value='ner')"
]
"version_minor": 0,
"model_id": "10bac065d46f4e4d9a8498dcc8104ecd"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": "Text(value='', description='Your input:', placeholder='Enter something')",
"application/vnd.jupyter.widget-view+json": {
"model_id": "ddc51b71c6eb40e5ab60998664e6a857",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Text(value='', description='Your input:', placeholder='Enter something')"
]
"version_minor": 0,
"model_id": "2c5f1411f7a94714bc00f01b0e3b27b2"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'word': 'Paris', 'score': 0.9991844296455383, 'entity': 'I-LOC'}]\n",
"[{'sequence': '<s> I\\'m from Paris.\"</s>', 'score': 0.224044069647789, 'token': 72}, {'sequence': \"<s> I'm from Paris.)</s>\", 'score': 0.16959427297115326, 'token': 1592}, {'sequence': \"<s> I'm from Paris.]</s>\", 'score': 0.10994981974363327, 'token': 21838}, {'sequence': '<s> I\\'m from Paris!\"</s>', 'score': 0.0706234946846962, 'token': 2901}, {'sequence': \"<s> I'm from Paris.</s>\", 'score': 0.0698278620839119, 'token': 4}]\n",
"[{'sequence': \"<s> I'm from Paris and London</s>\", 'score': 0.12238534539937973, 'token': 928}, {'sequence': \"<s> I'm from Paris and Brussels</s>\", 'score': 0.07107886672019958, 'token': 6497}, {'sequence': \"<s> I'm from Paris and Belgium</s>\", 'score': 0.040912602096796036, 'token': 7320}, {'sequence': \"<s> I'm from Paris and Berlin</s>\", 'score': 0.039884064346551895, 'token': 5459}, {'sequence': \"<s> I'm from Paris and Melbourne</s>\", 'score': 0.038133684545755386, 'token': 5703}]\n",
"[{'sequence': '<s> I like go to sleep</s>', 'score': 0.08942786604166031, 'token': 3581}, {'sequence': '<s> I like go to bed</s>', 'score': 0.07789064943790436, 'token': 3267}, {'sequence': '<s> I like go to concerts</s>', 'score': 0.06356740742921829, 'token': 12858}, {'sequence': '<s> I like go to school</s>', 'score': 0.03660670667886734, 'token': 334}, {'sequence': '<s> I like go to dinner</s>', 'score': 0.032155368477106094, 'token': 3630}]\n"
]
}
],
"source": [
......@@ -498,7 +432,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 14,
"metadata": {
"pycharm": {
"is_executing": false,
......@@ -508,46 +442,15 @@
"outputs": [
{
"data": {
"text/plain": "Textarea(value='Einstein is famous for the general theory of relativity', description='Context:', placeholder=…",
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ae68677bd8a41f990355aa43840d3f8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Textarea(value='Einstein is famous for the general theory of relativity', description='Context:', placeholder=…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "14bcfd9a2c5a47e6b1383989ab7632c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Text(value='Why is Einstein famous for ?', description='Question:', placeholder='Enter something')"
]
"version_minor": 0,
"model_id": "019fde2343634e94b6f32d04f6350ec1"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 168.83it/s]\n",
"add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 1919.59it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'score': 0.40340670623875496, 'start': 27, 'end': 54, 'answer': 'general theory of relativity'}\n"
]
}
],
"source": [
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment