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core.html
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core.html
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.zsl.core</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># 2020-08-10: unnecessary imports removed for ZSL to address #225
#from ...imports import *
#from ... import utils as U
import math
import warnings
import numpy as np
# duplicated from ktrain.utils
def list2chunks(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))
class ZeroShotClassifier():
"""
interface to Zero Shot Topic Classifier
"""
def __init__(self, model_name='facebook/bart-large-mnli', device=None):
"""
```
ZeroShotClassifier constructor
Args:
model_name(str): name of a BART NLI model
device(str): device to use (e.g., 'cuda', 'cpu')
```
"""
if 'mnli' not in model_name and 'xnli' not in model_name:
raise ValueError('ZeroShotClasifier requires an MNLI or XNLI model')
try:
import torch
except ImportError:
raise Exception('ZeroShotClassifier requires PyTorch to be installed.')
self.torch_device = device
if self.torch_device is None: self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
from transformers import AutoModelForSequenceClassification, AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(self.torch_device)
def predict(self, docs, labels=[], include_labels=False, multilabel=True,
max_length=512, batch_size=8, nli_template='This text is about {}.', topic_strings=[]):
"""
```
This method performs zero-shot text classification using Natural Language Inference (NLI).
Args:
docs(list|str): text of document or list of texts
labels(list): a list of strings representing topics of your choice
Example:
labels=['political science', 'sports', 'science']
include_labels(bool): If True, will return topic labels along with topic probabilities
multilabel(bool): If True, labels are considered independent and multiple labels can predicted true for document and be close to 1.
If False, scores are normalized such that probabilities sum to 1.
max_length(int): truncate long documents to this many tokens
batch_size(int): batch_size to use. default:8
Increase this value to speed up predictions - especially
if len(topic_strings) is large.
nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference
topic_strings(list): alias for labels parameter for backwards compatibility
Returns:
inferred probabilities or list of inferred probabilities if doc is list
```
"""
# error checks
is_str_input = False
if not isinstance(docs, (list, np.ndarray)):
docs = [docs]
is_str_input = True
if not isinstance(docs[0], str): raise ValueError('docs must be string or a list of strings representing document(s)')
if len(labels) > 0 and len(topic_strings) > 0: raise ValueError('labels and topic_strings are mutually exclusive')
if not labels and not topic_strings: raise ValueError('labels must be a list of strings')
if topic_strings:
labels = topic_strings
# convert to sequences
sequence_pairs = []
for premise in docs:
sequence_pairs.extend([[premise, nli_template.format(label)] for label in labels])
if batch_size > len(sequence_pairs): batch_size = len(sequence_pairs)
if len(sequence_pairs) >= 100 and batch_size==8:
warnings.warn('TIP: Try increasing batch_size to speedup ZeroShotClassifier predictions')
num_chunks = math.ceil(len(sequence_pairs)/batch_size)
sequence_chunks = list2chunks(sequence_pairs, n=num_chunks)
# inference
import torch
with torch.no_grad():
outputs = []
for sequences in sequence_chunks:
batch = self.tokenizer.batch_encode_plus(sequences, return_tensors='pt', max_length=max_length, truncation='only_first', padding=True).to(self.torch_device)
logits = self.model(batch['input_ids'], attention_mask=batch['attention_mask'], return_dict=False)[0]
outputs.extend(logits.cpu().detach().numpy())
#entail_contradiction_logits = logits[:,[0,2]]
#probs = entail_contradiction_logits.softmax(dim=1)
#true_probs = list(probs[:,1].cpu().detach().numpy())
#result.extend(true_probs)
outputs = np.array(outputs)
outputs = outputs.reshape((len(docs), len(labels), -1))
# process outputs
# 2020-08-24: modified based on transformers pipeline implementation
if multilabel:
# softmax over the entailment vs. contradiction dim for each label independently
entail_contr_logits = outputs[..., [0, -1]]
scores = np.exp(entail_contr_logits) / np.exp(entail_contr_logits).sum(-1, keepdims=True)
scores = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
entail_logits = outputs[..., -1]
scores = np.exp(entail_logits) / np.exp(entail_logits).sum(-1, keepdims=True)
scores = scores.tolist()
if include_labels:
scores = [list(zip(labels, s)) for s in scores]
if is_str_input: scores = scores[0]
return scores</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.zsl.core.list2chunks"><code class="name flex">
<span>def <span class="ident">list2chunks</span></span>(<span>a, n)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def list2chunks(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.zsl.core.ZeroShotClassifier"><code class="flex name class">
<span>class <span class="ident">ZeroShotClassifier</span></span>
<span>(</span><span>model_name='facebook/bart-large-mnli', device=None)</span>
</code></dt>
<dd>
<div class="desc"><p>interface to Zero Shot Topic Classifier</p>
<pre><code>ZeroShotClassifier constructor
Args:
model_name(str): name of a BART NLI model
device(str): device to use (e.g., 'cuda', 'cpu')
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ZeroShotClassifier():
"""
interface to Zero Shot Topic Classifier
"""
def __init__(self, model_name='facebook/bart-large-mnli', device=None):
"""
```
ZeroShotClassifier constructor
Args:
model_name(str): name of a BART NLI model
device(str): device to use (e.g., 'cuda', 'cpu')
```
"""
if 'mnli' not in model_name and 'xnli' not in model_name:
raise ValueError('ZeroShotClasifier requires an MNLI or XNLI model')
try:
import torch
except ImportError:
raise Exception('ZeroShotClassifier requires PyTorch to be installed.')
self.torch_device = device
if self.torch_device is None: self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
from transformers import AutoModelForSequenceClassification, AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(self.torch_device)
def predict(self, docs, labels=[], include_labels=False, multilabel=True,
max_length=512, batch_size=8, nli_template='This text is about {}.', topic_strings=[]):
"""
```
This method performs zero-shot text classification using Natural Language Inference (NLI).
Args:
docs(list|str): text of document or list of texts
labels(list): a list of strings representing topics of your choice
Example:
labels=['political science', 'sports', 'science']
include_labels(bool): If True, will return topic labels along with topic probabilities
multilabel(bool): If True, labels are considered independent and multiple labels can predicted true for document and be close to 1.
If False, scores are normalized such that probabilities sum to 1.
max_length(int): truncate long documents to this many tokens
batch_size(int): batch_size to use. default:8
Increase this value to speed up predictions - especially
if len(topic_strings) is large.
nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference
topic_strings(list): alias for labels parameter for backwards compatibility
Returns:
inferred probabilities or list of inferred probabilities if doc is list
```
"""
# error checks
is_str_input = False
if not isinstance(docs, (list, np.ndarray)):
docs = [docs]
is_str_input = True
if not isinstance(docs[0], str): raise ValueError('docs must be string or a list of strings representing document(s)')
if len(labels) > 0 and len(topic_strings) > 0: raise ValueError('labels and topic_strings are mutually exclusive')
if not labels and not topic_strings: raise ValueError('labels must be a list of strings')
if topic_strings:
labels = topic_strings
# convert to sequences
sequence_pairs = []
for premise in docs:
sequence_pairs.extend([[premise, nli_template.format(label)] for label in labels])
if batch_size > len(sequence_pairs): batch_size = len(sequence_pairs)
if len(sequence_pairs) >= 100 and batch_size==8:
warnings.warn('TIP: Try increasing batch_size to speedup ZeroShotClassifier predictions')
num_chunks = math.ceil(len(sequence_pairs)/batch_size)
sequence_chunks = list2chunks(sequence_pairs, n=num_chunks)
# inference
import torch
with torch.no_grad():
outputs = []
for sequences in sequence_chunks:
batch = self.tokenizer.batch_encode_plus(sequences, return_tensors='pt', max_length=max_length, truncation='only_first', padding=True).to(self.torch_device)
logits = self.model(batch['input_ids'], attention_mask=batch['attention_mask'], return_dict=False)[0]
outputs.extend(logits.cpu().detach().numpy())
#entail_contradiction_logits = logits[:,[0,2]]
#probs = entail_contradiction_logits.softmax(dim=1)
#true_probs = list(probs[:,1].cpu().detach().numpy())
#result.extend(true_probs)
outputs = np.array(outputs)
outputs = outputs.reshape((len(docs), len(labels), -1))
# process outputs
# 2020-08-24: modified based on transformers pipeline implementation
if multilabel:
# softmax over the entailment vs. contradiction dim for each label independently
entail_contr_logits = outputs[..., [0, -1]]
scores = np.exp(entail_contr_logits) / np.exp(entail_contr_logits).sum(-1, keepdims=True)
scores = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
entail_logits = outputs[..., -1]
scores = np.exp(entail_logits) / np.exp(entail_logits).sum(-1, keepdims=True)
scores = scores.tolist()
if include_labels:
scores = [list(zip(labels, s)) for s in scores]
if is_str_input: scores = scores[0]
return scores</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.zsl.core.ZeroShotClassifier.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, docs, labels=[], include_labels=False, multilabel=True, max_length=512, batch_size=8, nli_template='This text is about {}.', topic_strings=[])</span>
</code></dt>
<dd>
<div class="desc"><pre><code>This method performs zero-shot text classification using Natural Language Inference (NLI).
Args:
docs(list|str): text of document or list of texts
labels(list): a list of strings representing topics of your choice
Example:
labels=['political science', 'sports', 'science']
include_labels(bool): If True, will return topic labels along with topic probabilities
multilabel(bool): If True, labels are considered independent and multiple labels can predicted true for document and be close to 1.
If False, scores are normalized such that probabilities sum to 1.
max_length(int): truncate long documents to this many tokens
batch_size(int): batch_size to use. default:8
Increase this value to speed up predictions - especially
if len(topic_strings) is large.
nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference
topic_strings(list): alias for labels parameter for backwards compatibility
Returns:
inferred probabilities or list of inferred probabilities if doc is list
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, docs, labels=[], include_labels=False, multilabel=True,
max_length=512, batch_size=8, nli_template='This text is about {}.', topic_strings=[]):
"""
```
This method performs zero-shot text classification using Natural Language Inference (NLI).
Args:
docs(list|str): text of document or list of texts
labels(list): a list of strings representing topics of your choice
Example:
labels=['political science', 'sports', 'science']
include_labels(bool): If True, will return topic labels along with topic probabilities
multilabel(bool): If True, labels are considered independent and multiple labels can predicted true for document and be close to 1.
If False, scores are normalized such that probabilities sum to 1.
max_length(int): truncate long documents to this many tokens
batch_size(int): batch_size to use. default:8
Increase this value to speed up predictions - especially
if len(topic_strings) is large.
nli_template(str): labels are inserted into this template for use as hypotheses in natural language inference
topic_strings(list): alias for labels parameter for backwards compatibility
Returns:
inferred probabilities or list of inferred probabilities if doc is list
```
"""
# error checks
is_str_input = False
if not isinstance(docs, (list, np.ndarray)):
docs = [docs]
is_str_input = True
if not isinstance(docs[0], str): raise ValueError('docs must be string or a list of strings representing document(s)')
if len(labels) > 0 and len(topic_strings) > 0: raise ValueError('labels and topic_strings are mutually exclusive')
if not labels and not topic_strings: raise ValueError('labels must be a list of strings')
if topic_strings:
labels = topic_strings
# convert to sequences
sequence_pairs = []
for premise in docs:
sequence_pairs.extend([[premise, nli_template.format(label)] for label in labels])
if batch_size > len(sequence_pairs): batch_size = len(sequence_pairs)
if len(sequence_pairs) >= 100 and batch_size==8:
warnings.warn('TIP: Try increasing batch_size to speedup ZeroShotClassifier predictions')
num_chunks = math.ceil(len(sequence_pairs)/batch_size)
sequence_chunks = list2chunks(sequence_pairs, n=num_chunks)
# inference
import torch
with torch.no_grad():
outputs = []
for sequences in sequence_chunks:
batch = self.tokenizer.batch_encode_plus(sequences, return_tensors='pt', max_length=max_length, truncation='only_first', padding=True).to(self.torch_device)
logits = self.model(batch['input_ids'], attention_mask=batch['attention_mask'], return_dict=False)[0]
outputs.extend(logits.cpu().detach().numpy())
#entail_contradiction_logits = logits[:,[0,2]]
#probs = entail_contradiction_logits.softmax(dim=1)
#true_probs = list(probs[:,1].cpu().detach().numpy())
#result.extend(true_probs)
outputs = np.array(outputs)
outputs = outputs.reshape((len(docs), len(labels), -1))
# process outputs
# 2020-08-24: modified based on transformers pipeline implementation
if multilabel:
# softmax over the entailment vs. contradiction dim for each label independently
entail_contr_logits = outputs[..., [0, -1]]
scores = np.exp(entail_contr_logits) / np.exp(entail_contr_logits).sum(-1, keepdims=True)
scores = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
entail_logits = outputs[..., -1]
scores = np.exp(entail_logits) / np.exp(entail_logits).sum(-1, keepdims=True)
scores = scores.tolist()
if include_labels:
scores = [list(zip(labels, s)) for s in scores]
if is_str_input: scores = scores[0]
return scores</code></pre>
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